<%BANNER%>

Investigating Patient and Provider Influences on the Assessment and Treatment of Pain

Permanent Link: http://ufdc.ufl.edu/UFE0021521/00001

Material Information

Title: Investigating Patient and Provider Influences on the Assessment and Treatment of Pain A Novel Virtual Patient Technology Application
Physical Description: 1 online resource (115 p.)
Language: english
Creator: Hirsh, Adam
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2008

Subjects

Subjects / Keywords: assessment, management, pain, technology, treatment, virtual
Clinical and Health Psychology -- Dissertations, Academic -- UF
Genre: Psychology thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Pain is a misunderstood and mistreated symptom of acute and chronic illness. Patient demographic characteristics and nonverbal communication displays have been found to influence the assessment and treatment of pain. Numerous methodological limitations of these previous investigations constrain the research questions that could be addressed and the conclusions that have been yielded. The current analogue study employed an innovative research design and novel virtual patient technology to investigate clinical decision making for pain assessment and treatment. Fifty-four currently practicing nurses participated in this study delivered via the Internet. Thirty-two vignettes of virtual patients were presented; each vignette contained a video clip of the patient and clinical summary information describing a post-surgical context. Nurses were asked to make decisions in the following domains: 1) pain intensity and unpleasantness assessment; 2) positive and negative mood assessment; 3) non-opioid and opioid medication treatment; and 4) recommendation for a change in non-opioid and opioid medication. The patient demographic cues of sex, race, and age, as well as facial expression of pain, were systematically manipulated across vignettes and hypothesized to influence assessment and treatment ratings. Idiographic and nomothetic statistical analyses were conducted to test these hypotheses. Results indicated that at the idiographic level, patient demographic and pain expression cues accounted for significant, unique variance in assessment and treatment policies among many nurse participants. In several instances, the direction of the demographic cue effects was unexpected and counter to a priori hypotheses. Patient pain expression was the most prominent cue throughout these policy domains. Within-cue differences emerged in the aggregate; the size and consistency of these differences varied across policy domains. Exploratory analyses were suggestive of the role of provider education, professional experience, and practice setting on pain-related decisions. The current investigation demonstrates the application of novel virtual patient technology to the study of pain-related decision-making. These data indicate that patient demographic characteristics and facial expressions of pain often play a significant role in the assessment and treatment of acute post-surgical pain. Implications of the present findings are discussed in the context of the extant literature. Methodological considerations and future research directions are also discussed.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Adam Hirsh.
Thesis: Thesis (Ph.D.)--University of Florida, 2008.
Local: Adviser: Robinson, Michael E.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2008
System ID: UFE0021521:00001

Permanent Link: http://ufdc.ufl.edu/UFE0021521/00001

Material Information

Title: Investigating Patient and Provider Influences on the Assessment and Treatment of Pain A Novel Virtual Patient Technology Application
Physical Description: 1 online resource (115 p.)
Language: english
Creator: Hirsh, Adam
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2008

Subjects

Subjects / Keywords: assessment, management, pain, technology, treatment, virtual
Clinical and Health Psychology -- Dissertations, Academic -- UF
Genre: Psychology thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Pain is a misunderstood and mistreated symptom of acute and chronic illness. Patient demographic characteristics and nonverbal communication displays have been found to influence the assessment and treatment of pain. Numerous methodological limitations of these previous investigations constrain the research questions that could be addressed and the conclusions that have been yielded. The current analogue study employed an innovative research design and novel virtual patient technology to investigate clinical decision making for pain assessment and treatment. Fifty-four currently practicing nurses participated in this study delivered via the Internet. Thirty-two vignettes of virtual patients were presented; each vignette contained a video clip of the patient and clinical summary information describing a post-surgical context. Nurses were asked to make decisions in the following domains: 1) pain intensity and unpleasantness assessment; 2) positive and negative mood assessment; 3) non-opioid and opioid medication treatment; and 4) recommendation for a change in non-opioid and opioid medication. The patient demographic cues of sex, race, and age, as well as facial expression of pain, were systematically manipulated across vignettes and hypothesized to influence assessment and treatment ratings. Idiographic and nomothetic statistical analyses were conducted to test these hypotheses. Results indicated that at the idiographic level, patient demographic and pain expression cues accounted for significant, unique variance in assessment and treatment policies among many nurse participants. In several instances, the direction of the demographic cue effects was unexpected and counter to a priori hypotheses. Patient pain expression was the most prominent cue throughout these policy domains. Within-cue differences emerged in the aggregate; the size and consistency of these differences varied across policy domains. Exploratory analyses were suggestive of the role of provider education, professional experience, and practice setting on pain-related decisions. The current investigation demonstrates the application of novel virtual patient technology to the study of pain-related decision-making. These data indicate that patient demographic characteristics and facial expressions of pain often play a significant role in the assessment and treatment of acute post-surgical pain. Implications of the present findings are discussed in the context of the extant literature. Methodological considerations and future research directions are also discussed.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Adam Hirsh.
Thesis: Thesis (Ph.D.)--University of Florida, 2008.
Local: Adviser: Robinson, Michael E.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2008
System ID: UFE0021521:00001


This item has the following downloads:


Full Text
xml version 1.0 encoding UTF-8
REPORT xmlns http:www.fcla.edudlsmddaitss xmlns:xsi http:www.w3.org2001XMLSchema-instance xsi:schemaLocation http:www.fcla.edudlsmddaitssdaitssReport.xsd
INGEST IEID E20101113_AAAAGE INGEST_TIME 2010-11-13T07:30:53Z PACKAGE UFE0021521_00001
AGREEMENT_INFO ACCOUNT UF PROJECT UFDC
FILES
FILE SIZE 32042 DFID F20101113_AAAOFU ORIGIN DEPOSITOR PATH hirsh_a_Page_042.QC.jpg GLOBAL false PRESERVATION BIT MESSAGE_DIGEST ALGORITHM MD5
d7634b7c9affdb518fbf71df7c894a7c
SHA-1
6b467fd352cf51e46dc871a62e2716f9c559ea8f
1926 F20101113_AAAOGI hirsh_a_Page_044.txt
f44960624e2b152d55dc373eac3b8dc5
fb218b6c2f40ea5566544b4e1198a35cf5d5a59b
1053954 F20101113_AAAOFV hirsh_a_Page_033.tif
243eecc944c4cdd6a4aa009292b5783d
6375d03efae4718f3efeb0beec2d335773a575c2
36687 F20101113_AAAOGJ hirsh_a_Page_109.QC.jpg
5c7a077cf5337d1a344d11bc006fc2af
0ce78c2f4d8ea148a0baf9d1d2b7364926696f3c
21991 F20101113_AAAOFW hirsh_a_Page_052.pro
41fe0517e6318e60178121e13b57aaa2
d6e2f5fc7c22b230fd869ef21dc3bbbb274f8c57
66193 F20101113_AAAOGK hirsh_a_Page_113.pro
d7a5e04ade6029cf35978517cc191f8a
25495604110448c148aa9ece1a9f60a5f421c7cd
64755 F20101113_AAAOFX hirsh_a_Page_112.pro
8e18157475e4486fde9ec61a79a7e87e
57d67acaa437b3219207e974367d03366b130d38
109406 F20101113_AAAOGL hirsh_a_Page_018.jpg
6d63f91166a6290743eec2ba911758a1
8e53a7de13321fbe93af348747038a4d73876a9a
124704 F20101113_AAAOFY hirsh_a_Page_069.jp2
1611c5f8dc6e7d26db944bbd9c7dbb77
9649752c2ebfaefc9254cc2eca7e5d4064a04cb6
2165 F20101113_AAAOHA hirsh_a_Page_075.txt
6e9adf5e538099e6cd57ae0a9c2cb0f6
f0c089b32754e53a37695760b20846af3a6c4e92
110513 F20101113_AAAOGM hirsh_a_Page_062.jp2
a560a1c9f1644dfa3de99c1486f9b313
77c1fb6e30cb7fe143d214d19d6708e92f13d1d3
5411 F20101113_AAAOFZ hirsh_a_Page_006thm.jpg
0b7a2b154adaa9cfd9d8258f67d5d7d6
8fc4a513d2b67bb94a96fee87f3e7f84f3cb1786
2156 F20101113_AAAOHB hirsh_a_Page_066.txt
33bc0c5afbe105f78d0a13c0d35dd814
a51f03a1d6b59bb297ea57816daf39da387fe803
513 F20101113_AAAOGN hirsh_a_Page_095.txt
4188410b8db4add0d176f40bdcb581c1
f757c46e0a8368ce873c2b3ee79daab25e4160b0
79314 F20101113_AAAOHC hirsh_a_Page_098.jp2
72a3b1d6398de26e03b2e994e5a1baf9
9bfd445dd820c971db59fc3867f0116878cc13db
28876 F20101113_AAAOGO hirsh_a_Page_093.jp2
8fca66140a9ef234534e241d7d3c269a
fcf3911c07a36ec1d3705c96e99927d56f5d0e25
28808 F20101113_AAAOHD hirsh_a_Page_091.jpg
d7643f5bd79ba2c21615d466de440859
e12a074f1fa5f05e3fdda006421c98f92c3eea04
84005 F20101113_AAAOGP hirsh_a_Page_040.jpg
757685f50cb0a9889203bb0b04c0f9ea
4bbd3b0790a618fb7c51d9d3ed4d702d6ec292de
F20101113_AAAOHE hirsh_a_Page_007.tif
42deca7fc3afe0670274db27caaa43d4
636026cbd9eb5c551508f963caef78e2d50d001f
8930 F20101113_AAAOGQ hirsh_a_Page_001.QC.jpg
35d4ce6e16edb15c65c07f8c4523c11d
c400f45a30d6925378785bbf213802c9b3a0e2d0
115575 F20101113_AAAOHF hirsh_a_Page_030.jpg
48181607c8ff678017d0cd3ecca147bc
ca1534a6e1d014203a2d1b35f31cacba334657d6
121308 F20101113_AAAOGR hirsh_a_Page_073.jp2
aea39d6123b2b7ad5c21bfa7dd31fdc1
abd35b3729438ac07d63e2d954c78f913583d86a
27674 F20101113_AAAOHG hirsh_a_Page_089.jpg
a26c5bc299875be5e548ad4d94b5921d
f1ac6f5718978a3cbd162e6b8f4059b6e5531648
46863 F20101113_AAAOGS hirsh_a_Page_060.pro
8830778419e869d7ae9e06013506e9df
71905f35b324c6e49332b45a24373b94692f0653
50948 F20101113_AAAOHH hirsh_a_Page_054.pro
905da910ea89cb5d29d1a3057a46491d
3f22240abe42f5702e388e0f66fc0d58cca859ce
F20101113_AAAOGT hirsh_a_Page_035.tif
2be32feaa4059004a22d635c85936f7a
60ee8aab8e758d7095b9bcfb9e2cc5dc0e199f30
52439 F20101113_AAAOHI hirsh_a_Page_034.pro
da0aac2613d28811d45459495c5338a3
925d2d043c8159ccf3d088de1f51021dd1649032
8784 F20101113_AAAOGU hirsh_a_Page_074thm.jpg
1b03701755975f2faa8cb94e1907dd4d
16ca282a88c1cded451eb17f3ecc4dfc52968b52
119335 F20101113_AAAOHJ hirsh_a_Page_081.jp2
34042a9b92f23a83df1d04472b5190d6
cfbeab8e7ffc9da52015936fb85b6bf2ca477012
12083 F20101113_AAAOGV hirsh_a_Page_099.pro
d5133fc16dfef3c582989b1f75e4f49e
31a60df782528211faa36997712620c2e8967591
2161 F20101113_AAAOHK hirsh_a_Page_024.txt
94affef1b4a0a2f4277ae24e4ff7314f
036b1864d43c2d04e03447f019e02d873fac656f
5883 F20101113_AAAOGW hirsh_a_Page_090thm.jpg
befcf2f64e043749302ed89d4177f20a
4705189f477c492871e0f9122800ca9ffb86ee1b
2683 F20101113_AAAOHL hirsh_a_Page_043thm.jpg
272feca33f0e4f54c5ff6d83b4f19203
d66ef9d4f41cb61dab9dbe14054ffc9813872d7e
F20101113_AAAOGX hirsh_a_Page_082.tif
b240b449d6832fbed534b8ed407306f0
92c06be478086d7a28dd06edc72ebedd7a84ba42
F20101113_AAAOIA hirsh_a_Page_024.tif
1df786701d59d7e3d465efc8a6598ccd
27c14cf9e703173a0860b598466f75c5ae39e23a
35840 F20101113_AAAOHM hirsh_a_Page_048.QC.jpg
73d8dd8bc27eb0679dedab9d551a095d
1bf741912a5d00b48721da156b140432f04cf6f7
F20101113_AAAOGY hirsh_a_Page_097.tif
e65d38893682ab6a6e35ca0c469a375d
5cfd16eb53f803fc1012484b5ea45b38fe756fe4
112803 F20101113_AAAOIB hirsh_a_Page_057.jp2
e7cdee02d8be48e60e334078a6cc5ebe
15da573377984f4c483a6527b50fcc42d94470d5
9582 F20101113_AAAOHN hirsh_a_Page_001.pro
80dc770c11874798172c6e09ed41826d
a42d582ba000a2d0e487c6837e1db2b4d4f3c16b
22165 F20101113_AAAOGZ hirsh_a_Page_045.QC.jpg
92888e8498ef2a421bef2ac0dea967da
a97b7a7bae4d119dc9598654fe03f958bb1acc09
1527 F20101113_AAAOIC hirsh_a_Page_055.txt
d9dc9aed8715954f74d7f70ae8d8c1ac
7a65d02f069c72d04221b27fa05c4785848cef9e
2046 F20101113_AAAOHO hirsh_a_Page_054.txt
e0bd318eb356591f0c1adaacb83ae27e
2c0b0a9f29d2cc69790df867af9db1784b894798
38435 F20101113_AAAOID hirsh_a_Page_113.QC.jpg
34f34c5beccb2e2026a60fc92a7e8194
745041ee5f03e0b57fa3568cfca5c45fea1c8310
F20101113_AAAOHP hirsh_a_Page_078.tif
ef040d8ed19310502f4cec6871040671
7342f07b754e1f36b87bc80ffbddcfeda9e305f9
2238 F20101113_AAAOIE hirsh_a_Page_084.txt
f3a53c3b41e23ba9bba8981165a83d7b
5460d4922d4453b399fb05364cceae9c2c5ced99
9086 F20101113_AAAOHQ hirsh_a_Page_020thm.jpg
cb79bcf7f9867859ce7f49ac23d5f7df
e9ed31f6b46827051e00c1f9a28d0b19a150ba67
1113 F20101113_AAAOIF hirsh_a_Page_052.txt
ac5a8f68a6bf83e1f7b5228d5988c862
41c795b395ab1c893cb1323ec3c1c85b2b967966
2100 F20101113_AAAOHR hirsh_a_Page_034.txt
3ffa9038cdfb8a8bda69a9e4248fbc4c
876ca16144a284686eabf80051d12f115b18e63d
9155 F20101113_AAAOIG hirsh_a_Page_108thm.jpg
b3f0433a01f4af65eeb3f1960639c78b
91f6287bd98d452e4457ab5cbbce236d4f836b57
95 F20101113_AAAOHS hirsh_a_Page_002.txt
e411f8721bb59395c88be9d9c2f87fdc
4e763450590a0786f7d62b59251d650c4e8103b9
2246 F20101113_AAAOIH hirsh_a_Page_083.txt
0afc0ef409a596b94f93161430c890c5
57a978df6c68ad7de66153ac273ac590136ecfaf
2283 F20101113_AAAOHT hirsh_a_Page_069.txt
fa0a3892b74d3b9944ef38f70b8183b2
488695d8a4b8f8029cd4563802193c1f05564262
27109 F20101113_AAAOII hirsh_a_Page_101.jpg
24c5729ed5943862f31ffd747e1ba712
6fd41bee8996a6fda2ae72ba354eae985f182409
8625 F20101113_AAAOHU hirsh_a_Page_053thm.jpg
c5732fd59e3634bdc94a8a2062bceb9e
552f46f872191f465f3b594c400e062f4ad585f4
614 F20101113_AAAOIJ hirsh_a_Page_087.txt
b39d10ec9d725e85aab409e7d8a048a0
868f55b97a5d4b053f89df471466a0947611824b
113801 F20101113_AAAOHV hirsh_a_Page_080.jpg
c0cd393ef0b2db66a39d054ed8d5f916
bd558bdcc5f4943310a90307c9c4437d17a1c2b4
1095 F20101113_AAAOIK hirsh_a_Page_092.txt
65c70d95d46dbe86b395a647d1c4c7c1
f473e9aeb060afaff2007dcff37f1905f832982b
28104 F20101113_AAAOHW hirsh_a_Page_093.jpg
114d2634bd83436c6c0083747a4f7de3
088ce56ea65430ba72b71084a67d72eea49b85d2
2437 F20101113_AAAOIL hirsh_a_Page_089thm.jpg
aa45be35350fae52ed3b8c334f9fc42e
abde02c0c53c282f6166ecd01936c6d13f71e6f7
8435 F20101113_AAAOHX hirsh_a_Page_095.QC.jpg
ea783fe045cc5aa6badee013bd02c944
8c07ab719e6a9cdcef9f05bcf406b2bd0c5a711d
64433 F20101113_AAAOJA hirsh_a_Page_008.jpg
6d4cb53c69ac5aeb3cab0d650e7f714f
4f18f815238e48ed4fb992917a7ffdd9fe2b203f
2194 F20101113_AAAOIM hirsh_a_Page_031.txt
9bdc83767477fe1f327627845926138b
45beaa2be3ef432bc6d63378c13a6668076fc18f
119896 F20101113_AAAOHY hirsh_a_Page_015.jp2
867f65b6322f61779ea54ffa42906de5
212a167f6f38580561241c62c4affef62860707b
108704 F20101113_AAAOIN hirsh_a_Page_033.jpg
b669f0b73cb7f35b3f9d4ac16f1f460d
b7721490cf8b7a672b5a4d8f3651300cd8d81b5c
57145 F20101113_AAAOHZ hirsh_a_Page_084.pro
ea32be158eecd17310aafef9d4ee6161
c39b6984528f2ecd8bc648a0a66ae39bf959cc8b
104654 F20101113_AAAOJB hirsh_a_Page_009.jpg
7f315f6112e199b411c6ec62902372f9
3aafa7ebb834cb16afe5f6a722d0e57fc3822a10
21934 F20101113_AAAOIO hirsh_a_Page_006.QC.jpg
c3de4e4f560a73392ea4499356cdfc39
5ee950564deb6b0ada0f8837b322e7012ea2baee
112545 F20101113_AAAOJC hirsh_a_Page_012.jpg
30e0c30e702944c82d65e50f89272fc4
ac30128ab4e8e4ee487fa29df92c669e6964e9ee
111082 F20101113_AAAOIP hirsh_a_Page_031.jpg
d918e06d42150e91c12b5999d9b5707f
281ae94733bb1cc62e6e747d25d533c184ab2466
113639 F20101113_AAAOJD hirsh_a_Page_013.jpg
785ff87533bbc0b74e57964e2a440ed3
b3df93e5d9284cf1e04fde21d00cbaa8f8fdfcca
117269 F20101113_AAAOIQ hirsh_a_Page_070.jp2
8a3863f9da325df17f918639c4bb7814
fdc7d6f3f82881629209e391b9d2d97f65c0d4bd
114563 F20101113_AAAOJE hirsh_a_Page_015.jpg
832a4087f47f3384fbd3b32a2fdd1840
a9a5b37d522aab04b1adef77e8f70998c98a4222
18625 F20101113_AAAOIR hirsh_a_Page_003.QC.jpg
964e2526dc6434cdc5d6499c8939eef9
abf02d13ff6bffd1a474f5201d5b95e78e4d8855
112555 F20101113_AAAOJF hirsh_a_Page_016.jpg
bf28e7498249878220180cf1416cdd93
89780c93d3f09ede605317437e6e655d356da4fa
133084 F20101113_AAAOIS UFE0021521_00001.mets FULL
8ae0f8564de0311116efb272dd5ce1c9
71e9710fceb290cc85785c14c6d91e0be22ceb22
105361 F20101113_AAAOJG hirsh_a_Page_017.jpg
22f9641ff34324a5540e537734de1ca1
4daea72c2e05e65b40db1bca3092f87edd8e3f1e
108193 F20101113_AAAOJH hirsh_a_Page_019.jpg
4ecfeb49c49cf72a148e07753fa73d15
a73f7c9a23b282a36805799f75a901333ca2451d
115278 F20101113_AAAOJI hirsh_a_Page_020.jpg
dcd0a5c1ed9bd2215badbe4995e7aa9d
40a748efedce2871e06739f3fddb0b0636eea5ff
29420 F20101113_AAAOIV hirsh_a_Page_001.jpg
f7fa42adc8a33eb5ba5e17c461000910
2fb8a34bb4a50e0676c12bca2cc0f661bba7be32
110904 F20101113_AAAOJJ hirsh_a_Page_021.jpg
5d07c4826ed06c23ad743d2ac593761e
a760bef6092dd16761dae621a132f448f105e1b4
57138 F20101113_AAAOIW hirsh_a_Page_003.jpg
3996a1ecdaa8864015e2449881989422
bcbabeeffcf7e7f304883fe9e5b0a8f7548337e7
115989 F20101113_AAAOJK hirsh_a_Page_023.jpg
3d8769813d6ac0642cfac60da1ae95c1
1bd99a4e5d84f206656a66bfc64653b4e0aa7a85
130021 F20101113_AAAOIX hirsh_a_Page_005.jpg
9d9b792dd5b98d547e7d974691bc0834
e0d91a477224f93c7ad43dc2018b0492d43e260a
100996 F20101113_AAAOKA hirsh_a_Page_046.jpg
09e65f44ab73d5b30b6b5635c994c98e
07146f4c708375c4beb8a95d88c3a3d1575c79e2
112368 F20101113_AAAOJL hirsh_a_Page_024.jpg
41632147cc8aed8bf13fcc95fc1f8917
15392a4cf35839a1c0b76031551071141abcb915
77032 F20101113_AAAOIY hirsh_a_Page_006.jpg
e3eebdf1a2d6c7614f753b68740dbce8
2766313e7943ecfc349101d993fbb4e97f0e46fc
105738 F20101113_AAAOKB hirsh_a_Page_047.jpg
060603f8a2f469c670dad50a2543320b
2b7b3c759f211c44215ee096e0625ceb66393d5c
115847 F20101113_AAAOJM hirsh_a_Page_025.jpg
42d15891e9575bbd049cb29fdb267dda
5aef148d049a992f19a7ede85ef7f2a8ce883733
101505 F20101113_AAAOIZ hirsh_a_Page_007.jpg
900b3139b4bc0cb14f3595f8ff8584a9
10cc41014f69018a15b026aa80d8aae56534ed92
112698 F20101113_AAAOJN hirsh_a_Page_026.jpg
d97be5f7fc220b33971250209d310206
088861887a98ee17c9cc462608110c89929897a5
103733 F20101113_AAAOKC hirsh_a_Page_049.jpg
12c96aa7fd932fbedf672a9da829a9c3
b0fd50f712ef59355c3a29d0ed527535c6ad3d3a
114059 F20101113_AAAOJO hirsh_a_Page_027.jpg
a7c0ca7b5b93931e893a2edd5732d9e9
ccd48bd88661a00f155b9592fff85cdcecf9f740
108831 F20101113_AAAOKD hirsh_a_Page_050.jpg
a3619539b517b0a2b4fffbfeeb1200b1
49aa925f21f258a15b15ab89236b3b156985cbda
110593 F20101113_AAAOJP hirsh_a_Page_028.jpg
b8c6c4dfc63ea2cc0be59a4420262233
195f1d1e028d7d98fba61d7e56f4787b4d8bf602
113846 F20101113_AAAOKE hirsh_a_Page_051.jpg
dc2cf185bd23ad109aeb02889baf0195
17e939a6b27c4ac95b20e438fbe75c780817e523
109566 F20101113_AAAOJQ hirsh_a_Page_029.jpg
576a687f200db46149ef1fe698c8f8e7
ed430b41b564e546452a773b4d90ec47cd1d9b32
112417 F20101113_AAAOKF hirsh_a_Page_053.jpg
c9f2bebf7ba7febc2f5556f17de29712
da3c97f68a8645dd9e1222e37d6369fb5b8e1027
50576 F20101113_AAAOJR hirsh_a_Page_032.jpg
e1f33dccaa580eaad3b9521ed3c5fe94
4fcc816df31e30d96f73d3a6b45429438ab7be7b
101113 F20101113_AAAOKG hirsh_a_Page_054.jpg
9fbaacba8c73d8ef4e0c5bd13546cc36
471ba79598892a50bd40b7f8ed6201e04a0e6136
105183 F20101113_AAAOJS hirsh_a_Page_034.jpg
9b18bdf5fc982041612c9d0ebf891b36
9165ff94b9a3bfb53e2704e0d8a1a0e49fb87497
47772 F20101113_AAAOKH hirsh_a_Page_055.jpg
2856326f1d5b2763dadf4367b87ac56f
1138aaf9bc8450e6269f68aaa5d84841255b71cf
98757 F20101113_AAAOJT hirsh_a_Page_035.jpg
77605d2655d387ca2323d74d1d0ba856
dc9802c8163131246445fe7dfa17e76ae868b4b4
116015 F20101113_AAAOKI hirsh_a_Page_056.jpg
4125fe3ec610d1adcf46145820eb20fe
87f019c941e05dcd735161a496bab782f4140fec
114964 F20101113_AAAOJU hirsh_a_Page_036.jpg
07aca708593b40949aab03952d90e27f
a02671898f401b0a3e9e1d24357d6b7e154d30c8
105964 F20101113_AAAOKJ hirsh_a_Page_057.jpg
1d1e0daf6939f075db82ae0af2dc7bef
c516022e67d08122ad26887786274d4bfa57f294
110582 F20101113_AAAOJV hirsh_a_Page_037.jpg
6535ad5f6d7315def71bc03914cf9642
b02ed88572b8c711eca5c6e481aba187c4e9994e
105509 F20101113_AAAOKK hirsh_a_Page_058.jpg
7296a6ffaf5621827e69739041d6e0bc
f1238cf834a8e302ba2f93fa783836016ff6fd4c
104979 F20101113_AAAOJW hirsh_a_Page_039.jpg
1177bd4f586cbf30ef6b951067ce1b78
90590e71cbc88a8b7b9ec9c5925f0b6bac9e8039
102877 F20101113_AAAOKL hirsh_a_Page_059.jpg
492912b25069160855089f260871689e
ec97db781fbf3ea0a07f53bd373add11c7aed576
101145 F20101113_AAAOJX hirsh_a_Page_041.jpg
2f8e96a270e745bcdf5ec3e8677dab5f
b71b8bf3a97c63a5afcbe0d268185f6674dfd611
111350 F20101113_AAAOLA hirsh_a_Page_077.jpg
5fdfa24e21ec9781b93b8f6015e9aa2a
3a44bffbccc1b66395f0b2be5f4c6a830fbcf72b
103601 F20101113_AAAOKM hirsh_a_Page_062.jpg
6f1040d86ab1fbca2905b1b775834e8d
13f5b8e15b421fdcecb11794b1a464acbbc392fd
32478 F20101113_AAAOJY hirsh_a_Page_043.jpg
f9d78171e53a9ba0935932a12d5411ac
10b084028e33ebd66ee9c933bafa2d3a616a350c
114451 F20101113_AAAOLB hirsh_a_Page_078.jpg
75b349cf2a2484eb4e0e0955f465fc13
6669c2df1fe0021a5fccc0ee76769b51289eb0b2
25469 F20101113_AAAOKN hirsh_a_Page_063.jpg
dbad470c7c2f2aa09469d3c8392d0f10
b8081dc4e6cc43580a2ef595d56725b26fca6bd6
69507 F20101113_AAAOJZ hirsh_a_Page_045.jpg
49c904a2371daa93d770bd4c035b18b0
27affc02ffc693452dab4720db1fa06d7a2b097c
111446 F20101113_AAAOLC hirsh_a_Page_079.jpg
64e764f9b4e7a1bc7f9a6bb48ea5b1f5
dbe9a0d21e554592440707c795353ef0de0c555e
92328 F20101113_AAAOKO hirsh_a_Page_064.jpg
dd9f6e05ec3e27471810979f3a74f115
91cf714168e46dd09e437cf331551c168a0595c5
110376 F20101113_AAAOKP hirsh_a_Page_065.jpg
e596886dc182cadd6ab13a98307eb30c
ab6030ed2265b72d59907a2d7ecd3bfde7d624d5
111254 F20101113_AAAOLD hirsh_a_Page_081.jpg
4ab655d3dba9d1f2ae9752c6638da5cd
cb62d33980dc071b5d945abaf4a51d6d5868206d
110221 F20101113_AAAOKQ hirsh_a_Page_066.jpg
34ab1d1aad750a0a291af8875fe1967b
8cd02813af555c1bf29c22c8022997186b616667
111326 F20101113_AAAOLE hirsh_a_Page_082.jpg
2ce96b451434bf83378f63bd930952b6
95e730c0e5a85099d93423a734ab7693f3a3e1f9
105144 F20101113_AAAOKR hirsh_a_Page_067.jpg
bd6a68fc67452a59bb07ed64e7c3ef95
784b34e95c58562a972ae206993573391db25182
114076 F20101113_AAAOLF hirsh_a_Page_083.jpg
beb92db0fd868a19a44de281bb179d6a
67e264b63127f087e9ba96ba36a1f354e0b545ed
113544 F20101113_AAAOKS hirsh_a_Page_068.jpg
7e590c1bbcb69df9fd342cd63e9fb786
5eafe27ae2a39ddf774751e318447dca2406eb72
113134 F20101113_AAAOLG hirsh_a_Page_084.jpg
7b0f92d28e1dc4fe4cdb543dfa4bb1e1
5392eb4c7f65e52e31788115fd30188562764d08
116654 F20101113_AAAOKT hirsh_a_Page_069.jpg
440efcc7a65fb3cb69e025288dce7371
b2c0fab8334b4872442dad06534c44fd31c25b91
79104 F20101113_AAAOLH hirsh_a_Page_086.jpg
92db66b909a91ac7adb9f7e6e14ee5cf
3a512d9dbdb8ed44985ba3cac364d0238c431f34
110009 F20101113_AAAOKU hirsh_a_Page_070.jpg
8526422b04485467c4771d648bd5386f
8bb82f6253cd5c341f180c307c0a540f9580d228
79649 F20101113_AAAOLI hirsh_a_Page_088.jpg
e7856ac624dbd54b8a6fb6cd07bb3d6b
cddd29f29a99b58d2cf01825969d16d50f762f01
114970 F20101113_AAAOKV hirsh_a_Page_072.jpg
6bc3e30e053b0729035d0470a577d180
b3938da1d99078074802284083322615ef795f78
79617 F20101113_AAAOLJ hirsh_a_Page_090.jpg
ec4cf3273879a4f78d9cb25bdb1e2a8d
3c3ac0080cf81f9af3ef683cd2e334bf51356045
113224 F20101113_AAAOKW hirsh_a_Page_073.jpg
9a085235e969adae6285e9f50b343cf6
4bb9228b32cbe485758e7795fdb5e676336958e1
78658 F20101113_AAAOLK hirsh_a_Page_092.jpg
fc5848fa147ebf148fa04acf07281c46
ec8071534b79fecdb4394ac8a36bd5bbe5241524
109874 F20101113_AAAOKX hirsh_a_Page_074.jpg
5418fb0a6fe4fc3665f3e9621be889e6
62b213bfb373f6b43e11716831ccef1bd49d6e35
4844 F20101113_AAAOMA hirsh_a_Page_002.jp2
fdcf09f58504d3c5026c48e0e2394ebc
83b4286b6ec7a19ccc4bfd30b85a24a55080cbf8
27622 F20101113_AAAOLL hirsh_a_Page_095.jpg
63eb5df7a9f45c5e9e2a2ead50c99513
040d900410af2c3c3b1d77768261ab35a5284389
110504 F20101113_AAAOKY hirsh_a_Page_075.jpg
4027f4f2cd49fa497dfe080a0e94cf38
4cc675e401894d86938a09458ea051f71d8e8cd1
60100 F20101113_AAAOMB hirsh_a_Page_003.jp2
2cc770aa370c74048b79206b8289cfe1
6e97061f8a2fff502ffc8b35419c365ec3ece806
77725 F20101113_AAAOLM hirsh_a_Page_096.jpg
cbe4775e610cedec0ed272ddfb9adda5
4e1ca9d0870fd264fd54aa8bc6f6cc95bf0add5d
109536 F20101113_AAAOKZ hirsh_a_Page_076.jpg
f24aa764112759ed0a59ab591412686c
c2a61d910c643a41d064c0ecf135b1508599f7a7
1051977 F20101113_AAAOMC hirsh_a_Page_004.jp2
2a210f6cdf418350b843e5eba1cded6e
a0dca3d1b80f59327659a90e897e4e54db5075e4
77056 F20101113_AAAOLN hirsh_a_Page_098.jpg
2671d1ab587e1961a59c2e34b4518782
886440e71f8c2c6f83853f9621a65eacb7a660fe
1051983 F20101113_AAAOMD hirsh_a_Page_005.jp2
93483c582c81494b7834df6ff82be3d9
4fcda1f850d3fb6953b1228bd2c33a0906714790
27338 F20101113_AAAOLO hirsh_a_Page_099.jpg
91f0aa00100b5fc2f4b12367d2933069
c37ef1315f1cb717578da2526dcf90d80ba98183
132818 F20101113_AAAOLP hirsh_a_Page_102.jpg
0801f5f30833d7bb739fd24d0d6a6a51
e03df4343efdafb997d0410d2508f8c01594bb50
1051971 F20101113_AAAOME hirsh_a_Page_006.jp2
021489dc6d91bf48beae406bbe09e321
48d4961515136c252f27b922c917374b02db001a
128150 F20101113_AAAOLQ hirsh_a_Page_103.jpg
f7b0ce9d79480e52bb1a190c3831109e
700201c7b9f69e5bf1b7a3a3d92a9ae3320edb7d
105434 F20101113_AAAOMF hirsh_a_Page_007.jp2
8b953f1c0bc8a468bcc0a8bdabd61e86
df7ed5a905d14795d582aecb20027e98bcd89931
130468 F20101113_AAAOLR hirsh_a_Page_105.jpg
d43cf2a9f6747f19e7a294caceed482a
b342ef1d5de91d846e75a8cf8bcf032d2f2bf433
69293 F20101113_AAAOMG hirsh_a_Page_008.jp2
c3c692792eed4eddb53ba1b3b5898147
60ac9b41e610d7a2e4c8a2194caeedb5d30353e1
125063 F20101113_AAAOLS hirsh_a_Page_106.jpg
32aa6edab01183efc20d17d9c8d632a4
f304856fd4da4d532a4c74917e8658e8c0acf279
111494 F20101113_AAAOMH hirsh_a_Page_009.jp2
e390311fc2e92c55cb50ba846d698ac0
07c87f816d3e674bb9e3ee33a41cc868659e248d
122587 F20101113_AAAOLT hirsh_a_Page_107.jpg
6dd9d5e4070440b0be43123e327dc19a
9fbf0b3dbfec5fb65b74a998296ef55d16a1fa52
123613 F20101113_AAAOMI hirsh_a_Page_010.jp2
9584326b0f13b619c93e0d68abf10b52
e3c6e8c6bfb3fb7dd3b2c02495ad1fa4f91a67f0
124362 F20101113_AAAOLU hirsh_a_Page_108.jpg
df354d99c7bf96235f9b2535389e973f
f66e2545d59b7f3dfa179ce5021ec4f731aa7438
120388 F20101113_AAAOMJ hirsh_a_Page_011.jp2
544c58354699981bff58e0cc4a867f2a
24a0ca5fdf3e6b363fe0c506f32fb93402526a6a
134206 F20101113_AAAOLV hirsh_a_Page_110.jpg
92a5a9fdea47d9bb146b9a5de0436c94
cc2634fd1e04a6515847f6d1e717f37decef3d1f
121253 F20101113_AAAOMK hirsh_a_Page_012.jp2
04eeaa085d65247011c978a4c39b5315
81e8ea2a7831c02ed7e2371a2559d1c905c9a71d
122363 F20101113_AAAOLW hirsh_a_Page_112.jpg
41da3e63bcb96bf3b90f9255bf29c67e
c7a8d165ad73a8e1b7657706bc1f896f66e82560
117670 F20101113_AAAONA hirsh_a_Page_029.jp2
aa5b0c42dd37c1a7283a5a579a6bc7cf
b3d40ac00856a2b33461ff804c649e1e7eaec4da
120492 F20101113_AAAOML hirsh_a_Page_013.jp2
4c77df6b8b62140004d8c564760248b6
762be21e4e3995b56315d90194922a713adb8167
131989 F20101113_AAAOLX hirsh_a_Page_113.jpg
396027da9ce0ecd0d25ac1d0eef03847
6ecf1935aa3e6b7ace10abc0ba35d1452fe8de49
118268 F20101113_AAAONB hirsh_a_Page_031.jp2
d7d4d053d75238b27efaa33125a90b82
79384a4c0606bda906a61eca30e5e86d41aadfba
117807 F20101113_AAAOMM hirsh_a_Page_014.jp2
7b8fcbbd04fb8844f826fb2dc331b5ef
92aef86bb4099c660c97bdba8760d05df6b415ed
67614 F20101113_AAAOLY hirsh_a_Page_114.jpg
36662a8c2345cdfc39a3d862581d2dc7
7d19ac641ac736a10d6cad7d6a20bc158cd3bf9a
112961 F20101113_AAAONC hirsh_a_Page_034.jp2
0fdaf6a8660e8e1207e7a95b7fb88faa
49a3aed20a2ea8412ad40fb92bd83b5eeed9dca6
119736 F20101113_AAAOMN hirsh_a_Page_016.jp2
16eae48ad8f985f62dcf878a66d290f9
efb20711abd6e655b943fc887308185abe1d9031
27629 F20101113_AAAOLZ hirsh_a_Page_001.jp2
9c3d1e7f2fc2fc6ebb1176295958fa7f
5ac3d2d9423244d6dbd98542a6f1744275886d74
105928 F20101113_AAAOND hirsh_a_Page_035.jp2
709a044df2cf48cb0a0e5d8d033fa310
7a237e96641424216aa4ff9239fad94082d8a3a1
111994 F20101113_AAAOMO hirsh_a_Page_017.jp2
5e2878b499d95dc181cba4508e8a65e5
0d3de998b8f6f59ff0e33cc6648889ef42afb2b1
121930 F20101113_AAAONE hirsh_a_Page_036.jp2
33d2cc6f75b1ab1cdf3bfd9d67619c99
60f66e73c6ed6d19ecfab8b24a14841f813ce9f7
117701 F20101113_AAAOMP hirsh_a_Page_018.jp2
7450c89ac2db3afba64ee54a433b652a
e38537da8a14c67c91a2c1e460789e44555cef3f
116538 F20101113_AAAOMQ hirsh_a_Page_019.jp2
25156e92e9ff04cb6d6701d7533e97d3
b4eb0686fef4022db15a3bc69b9322510d66affd
120740 F20101113_AAAONF hirsh_a_Page_037.jp2
a095b2398771ff5a6bca60a6dcd5df19
cc2c32d41210c9f94cce341a770357e9efb3786e
122821 F20101113_AAAOMR hirsh_a_Page_020.jp2
0541271a7599ce5c16f48fcd755e7b93
a6bc2ce01fa98ec759a106cce31dac88661d3afc
118157 F20101113_AAAONG hirsh_a_Page_038.jp2
8b70b68bb32e92563d6934d52fc0ce99
f38c913494427c5e9cd2d15efac4c6f450cd2484
117469 F20101113_AAAOMS hirsh_a_Page_021.jp2
2485a6a810aa257a96ee7aca048c3b42
bdd4066b3ba432dd109de042ffc319b1e8b96381
111156 F20101113_AAAONH hirsh_a_Page_039.jp2
00ce52d77f57a90588a0c10a054e5b33
ad9b6cf813bf76e7d3ff29759a7854f567138f51
122046 F20101113_AAAOMT hirsh_a_Page_022.jp2
57c659f69b0dd2fa922755675c309cf2
1566c008b0f317436eef8da5c7206d3548ece4b6
89901 F20101113_AAAONI hirsh_a_Page_040.jp2
6ee1f98c30ea3c61f3eb73b31c3ec791
e74564afbc67f8dc868a9bc794e87bbe4724b95b
123528 F20101113_AAAOMU hirsh_a_Page_023.jp2
ad1b5b47409233aca2bcf5f3d5abb3b9
9cb8697778e2d0d0b9a82113c0030bfb0975b1f5
106396 F20101113_AAAONJ hirsh_a_Page_041.jp2
b12dc9fe158a3d9d57f157ff04f550ed
0b3e269f51a5d9b84041c7c631b27129c6c19688
118966 F20101113_AAAOMV hirsh_a_Page_024.jp2
30eff19b4c1c2ff6cf0537a4e9a352fd
4f42dce2936c6d9726d8f7674ba774bc3db974b3
106016 F20101113_AAAONK hirsh_a_Page_042.jp2
e5ca542b7f7fc4067350ef2f3d64f109
4b0fb9a3f4b722049946bf5d149244651cddf29f
123386 F20101113_AAAOMW hirsh_a_Page_025.jp2
121ca549dae0af7c731f073a1e589035
a93c0b45a99a7d1af8c5f8a8af47ee5a603e606c
34919 F20101113_AAAONL hirsh_a_Page_043.jp2
1c716e5b1362aa0a8693f1736380cd28
76637cff889ec27c2b32b0958e06afbba3b27730
120346 F20101113_AAAOMX hirsh_a_Page_026.jp2
beb90d677b15138b4bdea8ea726d3cf4
8b584e108e3330a35397e1faa8951a66c3a70d92
38974 F20101113_AAAOOA hirsh_a_Page_063.jp2
798173b9667918674e841c14d7d4201f
c1bd760e20e2ba4c46d6c4510d89792b71bd86e7
99234 F20101113_AAAONM hirsh_a_Page_044.jp2
d717b13ff14435d9890b0120c9261b9f
da352b3ee603656fc08e0d445cacfcde4503e009
118920 F20101113_AAAOMY hirsh_a_Page_027.jp2
b245efb028b000fc94d385de8aee6d77
1a3c0cd6d389e6c31cc7a092d8225d3de2129977
98134 F20101113_AAAOOB hirsh_a_Page_064.jp2
26a3c355756c2b41cc54bc92dffaf7f9
7466c4025e99f2eb0cdd384435c693ba9cc450d4
73635 F20101113_AAAONN hirsh_a_Page_045.jp2
6c796c634373d1fd6690d2869455f3a4
0cde3f578bf37a2ee535e42d2652f4f8279cbffd
118462 F20101113_AAAOMZ hirsh_a_Page_028.jp2
d5c7c9f5528eca914f12c2d25b06a825
e1827b0699b32d16e8c020eb3885411cda57c51e
117830 F20101113_AAAOOC hirsh_a_Page_065.jp2
3c81608e7b07ed0b766637dd46a991aa
aa27dff1f95ad9918cf566210a54bdf5a0c7384a
108177 F20101113_AAAONO hirsh_a_Page_046.jp2
8dd4d917166c46e604ff24e7b04e481f
9b556cc0b21b00c9aa8fbc34951871b97d77c14d
117779 F20101113_AAAOOD hirsh_a_Page_066.jp2
54cf604bce5f51b5c232866e583f1121
18d60cc8bcb9acce747157db52259b6df9e9e4a5
114399 F20101113_AAAONP hirsh_a_Page_047.jp2
23e6504f4d9fdb7af444593999180caf
19f7eff91fc283286a7305b63a63d48ef6fb6ac5
121432 F20101113_AAAOOE hirsh_a_Page_068.jp2
506d26f40ac5fbcc835a02d88aba8e50
ea27ca4c8fc5c2cf2fadede0ef31aea4fc01ffba
117497 F20101113_AAAONQ hirsh_a_Page_048.jp2
331e1304a060f8edf66d8c688e35dea2
fd598dca7b7332dbbe24d9bd3653182c825d19c0
121983 F20101113_AAAOOF hirsh_a_Page_071.jp2
c9d9c3564c2984bb87825c76c89115c5
e72256a1f94d3e4e855579db08b4f88b05522711
121752 F20101113_AAAONR hirsh_a_Page_051.jp2
b1e4e0909057549b066bb9c1d1f29fda
e1d2e823b53c7687387152bf498fe619bfca8499
54978 F20101113_AAAONS hirsh_a_Page_052.jp2
9acb0e3d74b43f3453a249d188d162c4
7c68c62a599755da909395771c2cf5fc1d8b2e88
121612 F20101113_AAAOOG hirsh_a_Page_072.jp2
d0ec953b44bc6e4974ac7532aaddd54a
32a23626b5e9bd5f17ce1dfc891e96b930685b76
122526 F20101113_AAAONT hirsh_a_Page_053.jp2
6f3703c98ec697600dd9851f3337e126
f9c93a424825dde24d8413d0a0a66c9d4b095b03
117245 F20101113_AAAOOH hirsh_a_Page_074.jp2
144ba7d53f738f69e7ff815a41e22b9d
d4af3968176131bed29eb314242d6a73fe3c187e
108959 F20101113_AAAONU hirsh_a_Page_054.jp2
8153c0c276f1d432a04a6977e5367c53
fdcf9f5d9495a97c30b19714ce835718867bbcb9
118937 F20101113_AAAOOI hirsh_a_Page_075.jp2
2b4841c4e2199cfac4c34c990ed2fea6
175a915e81b7e365142ee126e3c37e340f55fdfc
79191 F20101113_AAAONV hirsh_a_Page_055.jp2
d38b740fb7a4914f90274bf4d587ca12
ed95d969673ae2ca3fddd4871a69314bc960a56c
118316 F20101113_AAAOOJ hirsh_a_Page_076.jp2
9b987861f16bc267b6d57fcb3dcacab7
0735d3a1b13a66e56d4576b879cb8459c13e0620
111181 F20101113_AAAONW hirsh_a_Page_058.jp2
66431bd10f5afecb3d01da922cd93ffe
177c0b1c33b7734474e9324001771a4715f08447
118274 F20101113_AAAOOK hirsh_a_Page_077.jp2
ac96bd2c10e52789c6fa69d85b7828c7
7c4d7e859198083163b8b6d9c6f360f90604d57b
109973 F20101113_AAAONX hirsh_a_Page_059.jp2
360e36c8d8d772987a860fbce476becf
d408fa18c8f7e1ad414ccdadad4e51b7e8cf5003
135060 F20101113_AAAOPA hirsh_a_Page_102.jp2
5518bc1210b561dc5aa4fa22e0d9d4a6
4603df26ec5261d60567caeca28e88871a8f4551
122311 F20101113_AAAOOL hirsh_a_Page_078.jp2
ec543190e585c210a4021b211abe4845
5efb9db5e734e5c5c982722e74c855d785a13466
137988 F20101113_AAAOPB hirsh_a_Page_103.jp2
9ed791dfe6d8646afec8e4261132988e
1564e999ab9fb800d7f49435ee2b36d9c6c02a48
119209 F20101113_AAAOOM hirsh_a_Page_079.jp2
9ea818e2e07d58e9ec42ee7b352fb681
ff4c0ed6d10e106ae136d8a807353ffb6e0bfebe
103109 F20101113_AAAONY hirsh_a_Page_060.jp2
fc7a0492fccd1e5efe0ed9835ad31731
fcb7d186304bee7406475fdadd15b3e0bb55b0a4
131666 F20101113_AAAOPC hirsh_a_Page_104.jp2
2930529ed0e98cd843802e934d237f62
2214b58f92c8f2cc433e0bbbe099ed58985ab96b
121201 F20101113_AAAOON hirsh_a_Page_080.jp2
d44a9a1ed570ba8c25db1cdc3186523c
690be8e3ae083fca9276428ac59b22cd2396ccde
119854 F20101113_AAAONZ hirsh_a_Page_061.jp2
03488a667e48cf9b2194aee817fe7544
13533e7d42b967c1fee19aa6938131ffe814f0b5
137189 F20101113_AAAOPD hirsh_a_Page_105.jp2
e316ae1d51135b8dba0ecb69a1d6f0ce
3a552c798a422d38de6d02269b02bc4ef66eaa95
119151 F20101113_AAAOOO hirsh_a_Page_082.jp2
391846a8e8d9e62d53a6a4c061272185
9f268519a3e72b623bc1cca62f175a6f394599ea
135373 F20101113_AAAOPE hirsh_a_Page_106.jp2
205b55133db9e16e0a555c0b703064ba
7210d8881913f627a834ab23e9991962b7dc23d0
121171 F20101113_AAAOOP hirsh_a_Page_084.jp2
6ad0c85bb81643a55876e9558622ca4f
499d6c9596f158290854363a79b0524f78e7c416
129731 F20101113_AAAOPF hirsh_a_Page_107.jp2
cfb1f57071f7c1e4cdabe961afc10ac5
074e865cc0122c899b3e042cf64742d08a1af7e9
97655 F20101113_AAAOOQ hirsh_a_Page_085.jp2
0b3ed40c7fdc7a388181c28ec68c99e6
58e522cdea3627fb93ce309281c65978b2d7fedd
138200 F20101113_AAAOPG hirsh_a_Page_108.jp2
b74a7dc8eaf3ed5c11049c5fd12703b8
891c318120e69f408803a8f3bf11c719599c3062
85452 F20101113_AAAOOR hirsh_a_Page_086.jp2
9a42a8f5a72a7fa1cd6d512093bd320e
30629d87b1518e3caf821b558675cf2741d3ffc7
28735 F20101113_AAAOOS hirsh_a_Page_091.jp2
9debbe2e238dc500bdf7f0e11bc5648a
f4157bb042876a67f03f11cc472a51e4e95c7e07
132936 F20101113_AAAOPH hirsh_a_Page_109.jp2
a21fa8341e4e4c327489e4dc5bd1053f
fed456af95c2b7bb9b868830e19a89013ba9d7c3
85866 F20101113_AAAOOT hirsh_a_Page_092.jp2
4075d8709712c41130c35193d426c446
f7747877b472c37b707f01304eee8f39469ab929
141608 F20101113_AAAOPI hirsh_a_Page_110.jp2
630555f24a9ab1edda130bd98537e09b
5ccc8394756f7e810c064739c9bee7740d933e4e
78061 F20101113_AAAOOU hirsh_a_Page_094.jp2
c02b0bfe91b765df46d89e36f2dadd00
278d9f78408c057c959d54906c8e54ec53a2b7ff
134740 F20101113_AAAOPJ hirsh_a_Page_111.jp2
657cedae8e8e4a2f874422f843ae5d7e
55a6700b1b751b22c6bea23912a1481e1385ad57
27562 F20101113_AAAOOV hirsh_a_Page_095.jp2
1bf7e9678559e765864ced80d9590c5e
175a3e92f7361f699f756cbbe232661435ded459
135231 F20101113_AAAOPK hirsh_a_Page_112.jp2
ad28f0848120b3d86df50b0c410ad15b
da2c4da1a21d290c6c609f715c02e84ba6c5b12a
81265 F20101113_AAAOOW hirsh_a_Page_096.jp2
97b3f15f91878f61f86c08ea3dec1e03
d137df31405223feb7c85d94325c30f560401800
139443 F20101113_AAAOPL hirsh_a_Page_113.jp2
631ba529cd09d49b5e62f2d6e6727e65
a1d0bc5c698cc952c744decac9c38f1d596622c7
28341 F20101113_AAAOOX hirsh_a_Page_097.jp2
5eef55604ff02dbce2ed9dde3e8f347d
5f0d27278d37ded5f8285952dc8ef8583d4947d5
F20101113_AAAOQA hirsh_a_Page_017.tif
35434c55b88ac95968884a93cfca6e39
a5429b46bd2c1478e3b9e8ad7e0f33c451c0fd96
72237 F20101113_AAAOPM hirsh_a_Page_114.jp2
bf799696ea4b9a66b830ae3b00ca718a
1f09a4fa8c81c51d93c0203569c460579a8c3126
27513 F20101113_AAAOOY hirsh_a_Page_099.jp2
2ffbe5a8d143ac8791c3184fa7d1401b
56b20b8384b195b24f2e9de7368818fa2f62f258
F20101113_AAAOQB hirsh_a_Page_018.tif
d8f4fc9e59bf25a1cab0f9a39dcdd14f
d1df1602e056139edd79982ac6e050a65305b1a7
32047 F20101113_AAAOPN hirsh_a_Page_115.jp2
d880251ac33067bf20864cfc77a46dfe
1c62c6280f133350ca2d39d56ba95c688b08df16
28336 F20101113_AAAOOZ hirsh_a_Page_101.jp2
ec1522c2941c5222f8614d285b012eba
bceb692176d8b090e4955b7d61a8b510d895f8ed
F20101113_AAAOQC hirsh_a_Page_019.tif
e30f182e0fa095c5e60edc16d44b841a
73e9b4b701189d23a895364071acf1b5734220aa
F20101113_AAAOPO hirsh_a_Page_001.tif
da9cc17ec8f84cb74510a5065fab3b1b
126429f0d729c12f97e01bedb68b3b52b050e9dd
F20101113_AAAOQD hirsh_a_Page_020.tif
316d086c6f0f1943cb4e5ea8308927b8
92b9c7f3a8543f65fcb6ad3184bab12faa5bf737
F20101113_AAAOPP hirsh_a_Page_002.tif
df09e0e55bf6332150c193556d2adaaa
29d7b246b354e26f70ef26a9171048c3f3617b9e
F20101113_AAAOQE hirsh_a_Page_021.tif
1c5cb9cfea2fc1ae760472e6ed9da348
3038b83e289ab1d33c8373beba5ad03a902687d9
F20101113_AAAOPQ hirsh_a_Page_003.tif
e9d1a0c9c41935e702b73403b26fa3d0
fac1d40e983bcd4b68bfd40060b43566b0d4785f
F20101113_AAAOQF hirsh_a_Page_022.tif
166aea5fe767330d813ca742b385f517
f6378d76d1ac28c978b4e66c7ab5ae0f63a97c2b
25271604 F20101113_AAAOPR hirsh_a_Page_004.tif
66b8fb0108a8bc2b5601788655347d15
82102dee239c81f86b28e608659cebfd84612ac0
F20101113_AAAOQG hirsh_a_Page_025.tif
be2adc093141b0b9d6280938fba9f189
ceabbdc5e0f6df8bffb8289b7c0996443a58cc73
F20101113_AAAOPS hirsh_a_Page_005.tif
132b792b3fd919e20a53bd58e7a0651f
d23222ed9d2cbc8e362a62241adc403b03b53e90
F20101113_AAAOQH hirsh_a_Page_026.tif
f8aa0c116d99baf8e5bb2cf313f2db3b
5c228da8f822bfacca4ea3b51e79fae2df908027
F20101113_AAAOPT hirsh_a_Page_008.tif
1d51c06de00ad17bcfe52c28a9516e5f
17a78cba8503eb278ed2c2c99cf7301a76d56e8f
F20101113_AAAOPU hirsh_a_Page_009.tif
e9fdcc5e66bc040dd76ef7adb8f3b175
6b0ac33ffa0b12f757b3351b750277db5dd7aa70
F20101113_AAAOQI hirsh_a_Page_027.tif
6365470e16134ef331fe978e5f04b273
ee49107c58676c4b6644356176bff2619f84689a
F20101113_AAAOPV hirsh_a_Page_010.tif
c5f9c3a9cd39d8e9e956091575da4972
f572ec14a35d62d2f9c811e91c23519cebdb306b
F20101113_AAAOQJ hirsh_a_Page_028.tif
e92f3603f00db3de3530eab88fa902ae
06692f1b17e97317a593769db3ef5e57368d5182
F20101113_AAAOPW hirsh_a_Page_011.tif
ccbadf79f8a4f4f99cd26870e57beaf7
552363fbfacaae3b2ba673ea18be4eab4a4ccf74
F20101113_AAAOQK hirsh_a_Page_029.tif
fca9add2905127f8c130f53e991a4120
942d3a3c8f2efca4b6c3d25589cf6d1c94262fcd
F20101113_AAAOPX hirsh_a_Page_012.tif
202bebe4336042a017f62f58404d0e4e
e553bb830c31637ed2899bb80427d9ab9366ad8f
F20101113_AAAORA hirsh_a_Page_051.tif
98961ea6b8e0fc6922eb5a6b93c9073d
07e6a32471637baf5b7ef594eba999bc5ea54382
F20101113_AAAOQL hirsh_a_Page_030.tif
a09e37d9b270b5b7e91ca0d85971955d
fcab83de2f2fb1632f3fc8029539206793eb8e54
F20101113_AAAOPY hirsh_a_Page_015.tif
80590e196954cf047a12c512725a9d34
47eaf4b0682dae96b0175c3fe316ad8bf7adf21c
1054428 F20101113_AAAORB hirsh_a_Page_052.tif
c403e5da644a023d897cbe7a30eb7c01
6f406735e11b15461c12de99dc3fd0488c5b380f
F20101113_AAAOQM hirsh_a_Page_031.tif
4208531935a8846d341585210a29c16f
b6c093656d0c1667043fb874f97342f089527a83
F20101113_AAAOPZ hirsh_a_Page_016.tif
d873e6b3ec29102f51cddf9a107a48e8
b9f9549cf97b3274d8cf511b1a282f0801d3eaac
F20101113_AAAORC hirsh_a_Page_053.tif
81b02614cd91ce196a6f8827b3a633c1
318c277b07d85a5819ecbc8c8671b8d07ba28455
F20101113_AAAOQN hirsh_a_Page_032.tif
b630483a59f39938177f1e75e2c73867
4c487d886801fca41a1bba7dd199d932fef547df
F20101113_AAAORD hirsh_a_Page_054.tif
07aa2b956920d7147bd4130923f385c5
70dd743395a1be41c6937506791bd205d3b85230
F20101113_AAAOQO hirsh_a_Page_034.tif
95da90e6f25db7df7edda0854b424cf1
428b028bda0357c193a9d0742a849c0b2c727425
F20101113_AAAORE hirsh_a_Page_055.tif
332716cbddb2f65a3367277f9229dcc2
b133e0dbfcce67e22c1041ebcdeaf93a5feb2399
F20101113_AAAOQP hirsh_a_Page_037.tif
f5cbd9930d4d0e71926d41ad41f7e6d9
5bebc954c8c0ecd6f6e5ffc387adb3a20afb17c0
F20101113_AAAORF hirsh_a_Page_056.tif
3a49a7bb379755b35e0a8981cd8a1134
377325b704d6bd29c167e36ca694ab582c2be491
F20101113_AAAOQQ hirsh_a_Page_039.tif
c4e18319c43e83ad989d3866d909d2a2
e79f9a37bac56a9cabdd2fdd1ef420e228125c12
F20101113_AAAORG hirsh_a_Page_057.tif
6ac3ad0fa456549d8f21fdffe5001c6b
4d408d58775880fe5ca515a9ac29dbdce82f54df
F20101113_AAAOQR hirsh_a_Page_040.tif
1e6014f4fcb619541cce0d25e9e95923
6483409557a22a26346672d4f71414b4182d9175
F20101113_AAAORH hirsh_a_Page_060.tif
12f1caeb084cd3424eae62777bec692e
341bec57c3d690f1e5b3806f7a949436465741ff
F20101113_AAAOQS hirsh_a_Page_041.tif
ab93f0d7581b0fa66d341ac9a4e02b27
bef8a27926ce00e976c1e3a5b3506d930003e08f
F20101113_AAAORI hirsh_a_Page_061.tif
4b3b02fdcaea00d998cf80152a65af48
4ac276a557d40e738ca014f9e4a9b23fae2fc259
F20101113_AAAOQT hirsh_a_Page_042.tif
3f91a6d286634b0d256ba63324949c82
9f6852ecd7dfc0c08c2d4b5e8fb6f60c63499fcd
F20101113_AAAOQU hirsh_a_Page_044.tif
6f24f6bfec84929424fdb48e397ab986
63dba32fba213176b33d572425c3ad01826d08e6
F20101113_AAAORJ hirsh_a_Page_062.tif
43814bb9b6ba7a5e939bc78588975445
c9a3298622ec1120ce06b34c988653eadcf98254
F20101113_AAAOQV hirsh_a_Page_045.tif
51955413989604fd2ed4cea5528ff199
fd154532e56158da8be9bdefd25a82a7b9bc8cdf
F20101113_AAAORK hirsh_a_Page_063.tif
3408cb308245435ca098edd6f5015695
c97d78d9732f3529a214166b586573da3413f221
F20101113_AAAOQW hirsh_a_Page_046.tif
3f4a7360ba0f278595f55d3b637b73f1
586bb483e4a894f9ced1dfad15950e89a72864f7
F20101113_AAAOSA hirsh_a_Page_085.tif
ede7105ece6bf9fcca4573ef03ed84db
f8e04cb6c45ab2009f1ebb2852beb3d3dc8e1b04
F20101113_AAAORL hirsh_a_Page_064.tif
fcbb4059c0f77ed581da1bd39a0621f6
9617162ee71c16e9c20b498218d170a646db7a50
F20101113_AAAOQX hirsh_a_Page_047.tif
37b9bfb098f41869f44c9d20974d3a09
49ab363e485aeb851a129b161eb72e958398bab3
F20101113_AAAOSB hirsh_a_Page_086.tif
37f9ac0a8785de9fb8c4853b0eea57f8
6e39017d334a6e8e466bbbd7a58ee9e8f98e3f1b
F20101113_AAAORM hirsh_a_Page_065.tif
455de5b56b1d388dea03d41c2e633024
f7b03b0326cb01f3c2c6506bec2556ebffe2d3bc
F20101113_AAAOQY hirsh_a_Page_048.tif
e3536f8d543eaa3c58affdd6e0349d92
7cb9c0d71efc10b154e2eb17a129ff74e6663480
F20101113_AAAOSC hirsh_a_Page_087.tif
ef6cee8a84690dfb854d27f927d01ee9
892f91e4bc0d25818ce33d72a5ea20b8b38c5683
F20101113_AAAORN hirsh_a_Page_066.tif
e0023176de5303c191435eb3a96d9681
bfac8e501dbf18dd4ca5d659f09efead8bb8b332
F20101113_AAAOQZ hirsh_a_Page_049.tif
7475ac7248068c1996be04c896274c73
acad874491edb083b3691357dc506a51d2b84279
F20101113_AAAOSD hirsh_a_Page_088.tif
475072ed3ce231efda3ad962dbcfc57f
377c6b71b4fe3949180fb67cb2f2c4272bb61a39
F20101113_AAAORO hirsh_a_Page_067.tif
fb40e56f93f72fb233bb391c337d5c3b
9b858b411b03c9c4a5f26ff22c5ed12eb1f96f34
F20101113_AAAOSE hirsh_a_Page_089.tif
954b0a6d233a40a01067d87fd4dcbef3
7ffc7c0432a185f868d231542b10dd0fbfbfbe5c
F20101113_AAAORP hirsh_a_Page_069.tif
c4f77e6919e5314a5948620c4f6d558f
85d452c8a60009ed23991aa1f34f6f810d77df02
F20101113_AAAOSF hirsh_a_Page_090.tif
6583b320b4f31bca367b082535e55862
6389a979e0f9d8b4ecb26565bcd1d3346c3e24e2
F20101113_AAAORQ hirsh_a_Page_070.tif
84b9c26d2c5d464b6598cf98884d1ead
8572abd0e3857d9d533df402e22c9fee7b2a1a88
F20101113_AAAOSG hirsh_a_Page_091.tif
26577a4f93e1bd030e0d2ed50712bf6d
c47507b6fdcf2c6afcefd9558fe11362c71f38e6
F20101113_AAAORR hirsh_a_Page_072.tif
1f87a1554c64bef8a8b95f80f852697f
e730f3175b5ef9aad2302c7403eef95480d38b46
F20101113_AAAOSH hirsh_a_Page_092.tif
43e3c95df1136cdd3fa6d4dc04a9a7bb
2bc6748239de650c15d53846afe38b29753f8954
F20101113_AAAORS hirsh_a_Page_073.tif
81122a14525d0c1f792770762c70a7b1
6ea3afb5fcee2e48188f6a37fb8dd871edfb9ec0
F20101113_AAAOSI hirsh_a_Page_094.tif
6eb165ff230db281cbcdbfabca2e98b0
8b77ca0dae9d9c1f2860fd4136039d9f4d6f141c
F20101113_AAAORT hirsh_a_Page_075.tif
fce5bd2cf357e7f98d76c4a6a2895f59
2d474a2d180e395fcb894e0311e40a64b754a0c6
F20101113_AAAOSJ hirsh_a_Page_095.tif
79ddb68fd4ae7796a55aad23c7deac39
055510ff8a51a513441c38f302ffc003c1cecb0e
F20101113_AAAORU hirsh_a_Page_076.tif
4959ced5f00409960516aa32d392aa0e
2d187f6b1ef0bd692d2f696d4e7d62b8f829824f
F20101113_AAAORV hirsh_a_Page_077.tif
a3df72fe5690c736f1273be554049dda
ae607b9fa281a704eab02a44d929a33c8446075f
F20101113_AAAOSK hirsh_a_Page_096.tif
a32878f5ab848da3f82a6514a1b17206
939576aafb990a942b66554710da53cf7f29f15d
F20101113_AAAORW hirsh_a_Page_079.tif
fb0e1feb44f4ccca2289d41005a154f9
034bd11184392e527f85dec4d81b756cd9359c06
F20101113_AAAOSL hirsh_a_Page_098.tif
dfa8cda64774a4c72129939f44fc20ae
862a426323b4d547ffc691eff1a101ffba851d76
F20101113_AAAORX hirsh_a_Page_081.tif
911e2e4e2ff80c1d7940564bf346e631
b56a051dda53cb6f1a75a66e31a896143f0a06c3
74325 F20101113_AAAOTA hirsh_a_Page_004.pro
6e1f549a891672a417db6c899c41ae29
63c85cb1e49f3581cd32784b7f4de715531fafa5
F20101113_AAAOSM hirsh_a_Page_099.tif
bfa3fcf745e6da4c04ea9671b8ba09e7
38ffb1ea61ee69eaf01664e2633f9e08d77d00a2
F20101113_AAAORY hirsh_a_Page_083.tif
1a1b9635bdc7401ecd6a3dd12680663f
0ac7f6cab3d307fe92477c8688f19d5aabafd10d
77586 F20101113_AAAOTB hirsh_a_Page_005.pro
05803407ff825e01911ded8e1b69e126
59b93c27a8d99d7b7b3eba1ed3b011e56058ac83
F20101113_AAAOSN hirsh_a_Page_102.tif
b7968db3f3fb74f68e5bae6bc146cf05
04a8a48f3fc1460b1d952e5916507755409f9e19
F20101113_AAAORZ hirsh_a_Page_084.tif
b2ac973df7c82bc6ddcfa9dba78698e7
ed66a991ff99826b91955b3a544a423597e81066
38814 F20101113_AAAOTC hirsh_a_Page_006.pro
6036eef41c6e45142eb192c75077466b
bff2e5b1c962244be33a28ddafde3fc8156be039
F20101113_AAAOSO hirsh_a_Page_103.tif
b3e31c8b942cbb1519099acca7bad5ce
59cc907fd156189b06550175ad35f764370022d5
47859 F20101113_AAAOTD hirsh_a_Page_007.pro
bfc77189bf72e7b1110c385d8aca2d2a
bdd9ff00e1443c8ceb8e7003de0880d7f80220d1
F20101113_AAAOSP hirsh_a_Page_107.tif
9fda4bac540c1927af908aafc96aea57
71b9d5f740f0d885c8a4e33effc74fa0337f9c69
30642 F20101113_AAAOTE hirsh_a_Page_008.pro
1cf1f2194d88af3e0eab59377ca2dca8
a8ae9794d599286fcca520a7ae7a2995072d7274
F20101113_AAAOSQ hirsh_a_Page_108.tif
9448e8a5bb85a7a0f83a928b11ea3597
db0ba701b95df1c3b205703f2f122992d9238976
50575 F20101113_AAAOTF hirsh_a_Page_009.pro
df11ce0e0e00feb076fd62c99f352331
2c811c13037e94548bf648685db3ed4d519aa266
F20101113_AAAOSR hirsh_a_Page_109.tif
e294cffa4d9d34063272faf26d770bad
e4e895de041998a6ad3c483b14f8600d167b5d1b
57796 F20101113_AAAOTG hirsh_a_Page_010.pro
4164656740867dc958bbad5c896a677e
3c419f7e25b4cdf26887d311ed3458ea0efd3bdf
F20101113_AAAOSS hirsh_a_Page_110.tif
859f9535df562ed8e480fd3f0da72555
afe10e850bb53929ed6e8c21ad356e28f4144ea7
55564 F20101113_AAAOTH hirsh_a_Page_011.pro
0c577acc122466d0ddd6a5a430985252
9e08ae69b945afe508c01ea0f15a1e03fda9ed94
F20101113_AAAOST hirsh_a_Page_111.tif
54ac6b700b33598a38f4cbc2a7a54117
bd3759cc56314b89a1a5d11a36e2f5c66b960482
56007 F20101113_AAAOTI hirsh_a_Page_012.pro
693f2ebe6fb5f782c28b05d5851b54eb
64fa736f3d1710ff1c45a38410f6bc1edde7dfe1
F20101113_AAAOSU hirsh_a_Page_112.tif
6073f6154db484bbcf3f00ec96cdb744
638e2f9ae2736481f2dbc3065ca8fdd1f490610e
53977 F20101113_AAAOTJ hirsh_a_Page_014.pro
7f282248d592758ca082d3f524089b95
935756d182da30be5ddcb0cb9d8b4a3087e5373d
55725 F20101113_AAAOTK hirsh_a_Page_015.pro
ad5aae51a119a6605745c60080504429
f9f5788951b12e8d5fe60c9da5fa9ebf880bc34f
F20101113_AAAOSV hirsh_a_Page_113.tif
b588a061a55ef30eb3781627f51fd80e
72899abd541b0a49f1ee740fb9c47eeb4ab1bb9c
F20101113_AAAOSW hirsh_a_Page_114.tif
8f12ec98b6c5d0d91250a3df1c29c716
87bb29ea1953a8d0ee42523307b079016348c096
53645 F20101113_AAAOUA hirsh_a_Page_033.pro
eb19a492072080ba0c5e4aa36fd8311d
3b5b2339c8ec6be0fb5a9048a6837790d8e30e4a
55669 F20101113_AAAOTL hirsh_a_Page_016.pro
69a2362a23e568f71d8ecf1e94bad066
a4e08ecd8d5042fcf526b7d5ab6a0114df8f54e3
F20101113_AAAOSX hirsh_a_Page_115.tif
cb3286fff3e57c5c8b7d42a3d45394d3
89eba77a66bf8003dc3a78e23cfbcc01da7e8373
48019 F20101113_AAAOUB hirsh_a_Page_035.pro
de2fc144319844b2113e618bae76ec95
28a1491bec3d8eb75d7d08657cb3d0b5e0ff5328
51132 F20101113_AAAOTM hirsh_a_Page_017.pro
7feef0588efb7ef1392ca3e537ab23c7
b50353acb4ca2fd31cda8d7c29b3c2dca548b2b7
803 F20101113_AAAOSY hirsh_a_Page_002.pro
3c4d6ecdfb26ae4d3441f0222c0d2e9e
ad2499c3955c6f7bfe53e59b641f30a109105661
57463 F20101113_AAAOUC hirsh_a_Page_036.pro
6f9aa474a62a3927afe3d743bfceb445
40bebac1b9e7f59a735181570b79f3d5c0de2af5
53678 F20101113_AAAOTN hirsh_a_Page_018.pro
4cb84233bff648986a26c0fd162217b5
f49ff0ff6efb3b489a356623b53817fb76e67e00
26183 F20101113_AAAOSZ hirsh_a_Page_003.pro
c0421f9b382727f2dbc9bfedd818d114
8a440e80740348e98adcba9c1a9e14466a9eca72
55497 F20101113_AAAOUD hirsh_a_Page_037.pro
82f09b670bb3804f28f1d4e6545ff5f8
031aad1045a681eab334087b715c31ac6c3693bd
53974 F20101113_AAAOTO hirsh_a_Page_019.pro
30dccf0b670447974b44aa7a425bb5a9
fcdb8e652eea340d232e3d8a280030eeb54f5a74
51704 F20101113_AAAOUE hirsh_a_Page_039.pro
0073fe01144da8a6a3c6d8ec461dd7ee
b18367739c8bd3f06f23a983891c3c7866269433
56130 F20101113_AAAOTP hirsh_a_Page_020.pro
27a237e66c6cb1851d5da31fe6aba0eb
f407c3d26b921b116d07d01eb08b1827c3bfa42b
40996 F20101113_AAAOUF hirsh_a_Page_040.pro
d8ea2e04537409e1ed6ec2f4738cd3b4
5f85b454cd9f64c09c1d986472bfdd5a918e5c96
53710 F20101113_AAAOTQ hirsh_a_Page_021.pro
d85ff39599fab9d78152a6c3918cc982
425df417321daf6fbe339c1fedddf879b5c02f12
48710 F20101113_AAAOUG hirsh_a_Page_042.pro
6944d9548d07891503943b7a64ca950c
19294698f0bb8781df552dc7088c033dde034f9b
56089 F20101113_AAAOTR hirsh_a_Page_022.pro
b0b4b73f9ef1d8db3ad4cdc14ae26d89
39ebb6d94913af3a57a87778ca9a5e161864b4f2
9034 F20101113_AAAPAA hirsh_a_Page_083thm.jpg
0100ecf358ec214c7e0ab12d64220dfa
c7fc73e13af997b1bf649263183beb3c2945539f
14552 F20101113_AAAOUH hirsh_a_Page_043.pro
912c2859f58c33e60d288c057733a4ea
c9a50e64e06481cdc56e5f5b55a44ce28acd565d
56885 F20101113_AAAOTS hirsh_a_Page_023.pro
482189a46c334982e06badf4b571fffc
fbb3966b3b6cf2e0d279cdf8c7457393c7e3800e
9030 F20101113_AAAPAB hirsh_a_Page_102thm.jpg
03e684e0b68658161a4f10268ac6ffb6
6e407e44fb7e62ed62aabc856688deaf4a858e1d
44720 F20101113_AAAOUI hirsh_a_Page_044.pro
98f48f922f91dd12ece849adbf40d6d2
117fed7be64e09d8feb082d016503b4f7b9929a0
55043 F20101113_AAAOTT hirsh_a_Page_024.pro
a27df9ecd7933b5a5d961a807c83c456
b80945fd5a07934b15a5a5da0275fd9274621727
37090 F20101113_AAAPAC hirsh_a_Page_013.QC.jpg
7fd7da4e6f1468e00fdfe230d36327da
d34ac696c64024bf766d9ca8f6a44d3cd62a2100
32535 F20101113_AAAOUJ hirsh_a_Page_045.pro
9d0d470d00e89e2d4a543bc04390ed6f
890544437445062306365aee9ba04c774feaf67b
57682 F20101113_AAAOTU hirsh_a_Page_025.pro
eb0b686a134fa44d0fe1d1f8a3eefde3
e8691b30e3a0dfa0377a21881318e9ee0996a62e
5757 F20101113_AAAPAD hirsh_a_Page_045thm.jpg
8616146523f1c78052c998ae344ca98a
1c649ba851f70ffab9a4a12eb81a449cabf6da49
49885 F20101113_AAAOUK hirsh_a_Page_046.pro
b2828fddc4278d5c26daf0aa8026c55d
43a917d04070b25551b933b6a5abf3ba71a68fe8
55344 F20101113_AAAOTV hirsh_a_Page_026.pro
71901a6c366b244a07b9b7053464c0ad
cbd487cc83fcbd7eb9da989ab9ac79191e95deb4
9009 F20101113_AAAPAE hirsh_a_Page_013thm.jpg
9a16c736acaea62fe20f6611a2059ede
95ec43eee144162d1ab070dba50ee15d3c34c2c5
52696 F20101113_AAAOUL hirsh_a_Page_047.pro
a64033f843559851b70a01349671aa76
ee85c13f9e3b6d869d6805a8e404579cd43b04cc
55460 F20101113_AAAOTW hirsh_a_Page_027.pro
73e7e150813573701c70ac03b6cbb825
9b9a377585f7d2cd5a36950567d7923648454110
8263 F20101113_AAAPAF hirsh_a_Page_054thm.jpg
2d554617ef6b777b821319da008d1bf3
1534598a709a181dea2622eaa248748acde4b1ca
53890 F20101113_AAAOTX hirsh_a_Page_029.pro
9ff5fb0431496174a01acbac978d4889
01f2ae37c72a5ec58bbc6fc39dfdac3f265e1024
7207 F20101113_AAAPAG hirsh_a_Page_064thm.jpg
8cb02da8900d7977bdbcccb2f3b11296
ad234e49096f5cc979aa4bdf9da912cf7d2b3b00
58353 F20101113_AAAOVA hirsh_a_Page_069.pro
f41a8fb3a9c600f2b83958dea63ba253
767a613385050fd7829ada24c78a1d0880d2ade1
54533 F20101113_AAAOUM hirsh_a_Page_048.pro
9f7d088449a54575537bb5baa26d7b6b
57c5e0c22f2d4bc0b25ead015fea617825657dae
57306 F20101113_AAAOTY hirsh_a_Page_030.pro
9b3b540d7cdb4048218ffea6395bf7ec
c8b4656e97b8df2cd0a45cab973a1844e2498401
6511 F20101113_AAAPAH hirsh_a_Page_005thm.jpg
06190142571ea8550879179812659041
3060aabe836e7e856bae3154f9c9ea26e2746f54
54887 F20101113_AAAOVB hirsh_a_Page_070.pro
1b98ebc5cd416e7c412f969738bcb51a
48c779e20a5a60a04722eb443f3c11433c691e3b
50925 F20101113_AAAOUN hirsh_a_Page_049.pro
23ec230c7cb4946036455d9d984e1c1d
cad33612a1b6177981ff5b2ef9719ed7144a2075
54892 F20101113_AAAOTZ hirsh_a_Page_031.pro
6f9c571e5578421544e56e839a24f34b
29320ead5d040b932abaf54468d526e3958229ed
564 F20101113_AAAPAI hirsh_a_Page_002thm.jpg
92df5907020e3a7bbf92ba1c9e5e71b2
c41a95b6e15dc763db8baebca381d8df11634e72
56530 F20101113_AAAOVC hirsh_a_Page_071.pro
e0e5f6466272d69a4fe620e7518ed338
1be809abe17a0fe3bd60e1a97dbf1f6ab2015211
52888 F20101113_AAAOUO hirsh_a_Page_050.pro
6ba582ffa9c43e7355fe4b905548c160
38445ab778edaf2e3a2bd6b30714a73111574d0a
8591 F20101113_AAAPAJ hirsh_a_Page_049thm.jpg
cf68dd5d44d25def1f86d39b71e324c3
202a6218e23a88934973dff05f4085156fc1c084
57336 F20101113_AAAOVD hirsh_a_Page_072.pro
5292659b59feb753913d96eb656b8988
1c8743ee6aa91808bac0bcfe0bf799f6a4c9cfc3
56364 F20101113_AAAOUP hirsh_a_Page_053.pro
fe6365571cb394e8fdcbe2aa94967e58
9cff45e71e8c93b208310ee0d11a15f35aaf809f
8398 F20101113_AAAPAK hirsh_a_Page_034thm.jpg
8f905f7016a6629e87248e3724a23c95
930d77811ae91f369cb3f9cfca5251334ab0aa5e
57172 F20101113_AAAOVE hirsh_a_Page_073.pro
ed429b78167a747dbfdd812566276c54
1554227f5663c8613eae948a8a8ff03f1d084986
36334 F20101113_AAAOUQ hirsh_a_Page_055.pro
86acc12bce9c69cd71088f5d47bd0313
45fad6e17d069c706dd7aaae56835a745d3408d2
8852 F20101113_AAAPAL hirsh_a_Page_065thm.jpg
f6b81bebdb6d0ccfb7e0fd2f9bc945a4
4792c1f47a8df351a9c77b7857ea5d60b4f1c9b9
54644 F20101113_AAAOVF hirsh_a_Page_074.pro
479b433752d9f8dbaba51bc07d429e3a
60376bb62d90d278fbd22f0fb3506a4b09bb912f
57433 F20101113_AAAOUR hirsh_a_Page_056.pro
f8195f8d6ab72fe69495692e2b72c6f6
8fd2ef48279291e0d8ca8ffb385fec640fa01447
34867 F20101113_AAAPBA hirsh_a_Page_058.QC.jpg
1bb1abef4b1657e181ee250363de3a6d
8f32416b8f467cbb4be0f1550a0183860c072306
8802 F20101113_AAAPAM hirsh_a_Page_028thm.jpg
7acdebae7201027878360919c7d4f3d1
44edf40bff9e9548fa82aaff4d37b1c47d499d7d
55093 F20101113_AAAOVG hirsh_a_Page_075.pro
58b642a71c1478816cdf82aaac3fbbb6
b8f4cdc3ce02b74fb40f353cd6c27b6346b2c85c
52864 F20101113_AAAOUS hirsh_a_Page_057.pro
f91f0d0a55d63ff527091ae3fb9e8547
fa6e2abac1736de97faba8bea9a13901bf16edce
8656 F20101113_AAAPBB hirsh_a_Page_067thm.jpg
1b68ba6db3ac945ad536aabd5b47667e
d637cfc5b6fa2c51bebbb37a151e2d38c3b88ba3
9047 F20101113_AAAPAN hirsh_a_Page_091.QC.jpg
7f10c1417e72ac6cc82f067d1ad1ce76
c07d3813d6c8269f75769c0dcd57e191d1900147
56132 F20101113_AAAOVH hirsh_a_Page_077.pro
5f9e62f6ae0c8b3d742389d28db684d8
b00154a3bd6b449d6da750911e8b416ceeeb7313
51707 F20101113_AAAOUT hirsh_a_Page_058.pro
97fe0da836b1a333541e4df5bd78598c
ffb07de63f8b3b106b7e8bd41669f77faebe6b47
35966 F20101113_AAAPBC hirsh_a_Page_082.QC.jpg
244eb6c4a55fd8cda58f78ecbdc6df15
b36a0941ae2655dadecae6b2577d5a15e907ccb5
35159 F20101113_AAAPAO hirsh_a_Page_107.QC.jpg
70bce76df6f29c8fca3174d9ed798fb1
02c39ba6d4ead39cb284b6713dc19492f38f0234
57100 F20101113_AAAOVI hirsh_a_Page_078.pro
fee95011aad6e80cf9047a858995cb27
f6f0f9a98e3a0be319c3537da215642a384ef3c5
55207 F20101113_AAAOUU hirsh_a_Page_061.pro
7cc5c4faf72189858a7929ad3774b166
0852c7a734b4515ffc15ca45757fc1196720608b
2416 F20101113_AAAPBD hirsh_a_Page_091thm.jpg
b970e88a510cb9075fe4a2e4152ff903
68e8ba9bf03f03fa09c0bd9689830fff3392aded
26514 F20101113_AAAPAP hirsh_a_Page_040.QC.jpg
9194ce4e773269b82980278d6b55320c
e4b2682cba630e20625f27cdf49fa21dd92d4b93
55515 F20101113_AAAOVJ hirsh_a_Page_079.pro
55d14b9bfad16953b3cc0063e4756b89
4804bab19a07b430b06b2ccc9128f7cf9a280285
51137 F20101113_AAAOUV hirsh_a_Page_062.pro
6068495da703867c40a64b33674286dc
b6d9643125de5800980acef16ff0666831f25d70
9279 F20101113_AAAPBE hirsh_a_Page_068thm.jpg
58850d50e787721b878cbd9ad99036fa
e402bbe98cdd0084dbc51bb3399d800456282f61
5775 F20101113_AAAPAQ hirsh_a_Page_004thm.jpg
e4c99129a10f067fd9dab9b10bf78626
bbad2f32122fa3428beb57df5129e2da1b996753
55432 F20101113_AAAOVK hirsh_a_Page_082.pro
7e2ead008a2a407b2b0d95aed196a92f
69a8bae8de0cb29de4cf43ec2809b6666a0ee8e1
55130 F20101113_AAAOUW hirsh_a_Page_065.pro
e7dc3552cba4da4bb626e7d5f8aebd45
f4bd7ff2bd0f8e7010373c36f01e93bbb6d049e5
8532 F20101113_AAAPBF hirsh_a_Page_047thm.jpg
c85f35e4e9ac165afaf1c4d15503ca92
9b4fd67e7fe2995d05625b3d5b1c702dc04e5473
8910 F20101113_AAAPAR hirsh_a_Page_014thm.jpg
fe4d0998e0deb2901a7efb1afccc90cb
843ba39d719ccd11ef6af3d4bc71c1937d84a18d
57422 F20101113_AAAOVL hirsh_a_Page_083.pro
3c97365a1d0c691b4ccafa94af517a58
481530ea0fb88fcbd67b46b1427b2f31222f00fd
54992 F20101113_AAAOUX hirsh_a_Page_066.pro
b109fab1cdb53a3afdb342faa315de45
d4977d14bc22f980e20c949a6243e666bd21b67c
33276 F20101113_AAAPBG hirsh_a_Page_009.QC.jpg
4c2081c4cb4a88e5032e315be8b1d27e
78a86712515ed53507529f1d9a60c1863877b5da
63241 F20101113_AAAOWA hirsh_a_Page_102.pro
2429b4c552675605a3a6fe074db64f0b
938cec51616aab2e8395f07d72cace2be2d52df7
2828 F20101113_AAAPAS hirsh_a_Page_087thm.jpg
5ab0aa65f368e55e7a97ac4dffca1143
5db89999084ad5eeeab2c6879b44dda53edf8014
44955 F20101113_AAAOVM hirsh_a_Page_085.pro
673f704eb28862fc17afe181c8bc82ee
bc5c5fadad9429a70469a4e52145a2374ab2e816
53203 F20101113_AAAOUY hirsh_a_Page_067.pro
bc53d6981a6de22e9afc56a340933fe5
cbe82fff575406a5951476eba35c7761c51867d9
8644 F20101113_AAAPBH hirsh_a_Page_021thm.jpg
4295f91835a0be8472d4214d7b66408e
66b377a32857b245a390b2ddc1fc91440c531579
66557 F20101113_AAAOWB hirsh_a_Page_103.pro
ab18803dcb60f913127eb1d3e0fc5f35
668e2fe1d0604e33f2fc39d931f5f78e72920f4f
9137 F20101113_AAAPAT hirsh_a_Page_084thm.jpg
4edbad39d61b30d63dfbadf1cc65e047
aab2662b68947b770a0259bf69d250987db9d732
56645 F20101113_AAAOUZ hirsh_a_Page_068.pro
a3a41dc26b7cc17c02fc5442d8ef485b
71ea1e8437ef7d14dcc74657e9d95da24f5ec1d5
9010 F20101113_AAAPBI hirsh_a_Page_061thm.jpg
e244adfd84f1f27b5525065005c308b5
27c074c27024c7ee1d9da17e5babfe854d3c71ae
62617 F20101113_AAAOWC hirsh_a_Page_104.pro
9abba0c0517fb0ffe09d1ccfd9673fe8
811b4f904774810460393f21045dd46fb1f15595
37431 F20101113_AAAPAU hirsh_a_Page_105.QC.jpg
9202d5b42b366371c4ef177838ebc36a
b5967b9e6202f762c1f49e83f187325289085cdc
15549 F20101113_AAAOVN hirsh_a_Page_087.pro
e8be5828d65b30f6f9dee6520ce04648
4bb95fbc4b723c88afd4af8aead379d14d5243de
8223 F20101113_AAAPBJ hirsh_a_Page_058thm.jpg
9036320c116aab308fbfdecb6b6eb8d2
d3f7d36c24f6fd9f12110a40e6df9a9519b26235
64642 F20101113_AAAOWD hirsh_a_Page_105.pro
9b284dd2540bb177fba02ea823b54201
7305612e9b36744e7c9e135decba72a17f04f89b
1430 F20101113_AAAPAV hirsh_a_Page_002.QC.jpg
299e565e9e6cb796503c7ea932d1d2a2
6a1cbe3898355c25b306ee8e49fad4a6fdade168
34113 F20101113_AAAOVO hirsh_a_Page_088.pro
0774e945805da4edb9fc6fa2fd892fd8
3510cafc41a719e0ec72373f9e0cf338ca5caab6
8899 F20101113_AAAPBK hirsh_a_Page_019thm.jpg
acdcab864e889c117b322cdad2eb4359
86d25da0344a0d985449f5c865cf62c0a04c727b
64569 F20101113_AAAOWE hirsh_a_Page_106.pro
40a5c68a909f5bdcef5befc8dd8f93a8
2ee88c90ce53d3d44c64b2f35c2ff05e3d53c2b7
2263 F20101113_AAAPAW hirsh_a_Page_095thm.jpg
7acc8f9f7eeec7512f09a52f63c3f4f0
e752de5d079e5a954ad8be43d98f24733f25f69c
12270 F20101113_AAAOVP hirsh_a_Page_089.pro
be94ee8b22b9bfc6bd1473bc7d4c9b08
ebae4e4649221dee2badd5722fbe651707d120f2
8726 F20101113_AAAPBL hirsh_a_Page_057thm.jpg
c1e94ef81ba99d0a2d3cce8c4f850f49
3ea6f4e488b1afe812b972c34359774b686f0d6c
61039 F20101113_AAAOWF hirsh_a_Page_107.pro
22d52b4a87bba6d47d992c62da155ef6
4eac9ea11dbe2707923f9c2f13e196c2c144be6f
33485 F20101113_AAAOVQ hirsh_a_Page_090.pro
80b016d60480e9b285c7a2d565fc7171
0d1f00d7b8a2e976e4a7d14661b19f5793d49b44
8895 F20101113_AAAPBM hirsh_a_Page_010thm.jpg
97232f3beaa669d452fee7653fc266ea
027a2e93c66045c465f43b6fd7bdda55ab96bc38
65568 F20101113_AAAOWG hirsh_a_Page_108.pro
66a12f378d11d9c18979b1ca062d5ffa
bddd6118cb4290a1678d403bab07ea53d35fd3f0
7282 F20101113_AAAPAX hirsh_a_Page_085thm.jpg
186ae4254411921783953184244a9695
93dc99f2d60cd2ac2fc1bdb8fd3a0facd71a50fc
12831 F20101113_AAAOVR hirsh_a_Page_091.pro
4c64cc5495c98ed563032dc191b02c3c
bede3263b18e0412fd70e3c948227c895505629a
8931 F20101113_AAAPCA hirsh_a_Page_027thm.jpg
f0420846c8d46d7b7e297f3af1c01d95
a16fd8e8b19cc1346fb6da177f075133815636a0
23241 F20101113_AAAPBN hirsh_a_Page_004.QC.jpg
972013422621f7114aae1f951a0bc029
5fc0c783e39c364b707bda0216843c2d8c2af878
62301 F20101113_AAAOWH hirsh_a_Page_109.pro
ca9460e194c7e6fb86940a9c57d4fa7f
2873d723d42ff437021253f42376db90fc3c929c
23874 F20101113_AAAPAY hirsh_a_Page_088.QC.jpg
6972a33eb0bb9975d50d718deb6b8b96
03d1276ea36656d7f9fa4ae117df57a4e4307f60
25294 F20101113_AAAOVS hirsh_a_Page_092.pro
04542db969aca79d4d5f78d0d6051f13
ce841d07c36deb1d644ff8b8f6fb8882f6fcceca
33413 F20101113_AAAPCB hirsh_a_Page_046.QC.jpg
3c31b77f14d9b1fa2dd7fd44e48af1f8
a33762214f78f85a74aa77af4eb6c7778452c9df
32644 F20101113_AAAPBO hirsh_a_Page_041.QC.jpg
05391f5310b691ea4937ed1cf6a96095
c575639ac9bb3935cbde97a270f815e02404d121
68739 F20101113_AAAOWI hirsh_a_Page_110.pro
aef7537de7fe8eb117aa4ee30fc6ed4c
56932c86a3ef73b77d6ee822158f8b243fb91a96
7680 F20101113_AAAPAZ hirsh_a_Page_007thm.jpg
b633bc990629526fac2ba45e7ca88617
f5998b79850fbdbfe0830632c80d4e4a6ae6aa19
12321 F20101113_AAAOVT hirsh_a_Page_093.pro
5fe63d780e865e9507db8ce212ce0cfa
949e6a2225c0bfebafe6fcbcce9212b55144562c
38760 F20101113_AAAPCC hirsh_a_Page_056.QC.jpg
050786dee3f831cae46e013faa945dde
f01633c333b7940659234340ae0f371ab103fc5d
36806 F20101113_AAAPBP hirsh_a_Page_051.QC.jpg
aa419ad416cbc931c8d5822c9b1ed18f
30e3704c58c3b8f09a0a3b02167609dc9e69df6e
62455 F20101113_AAAOWJ hirsh_a_Page_111.pro
69f847db95293d02071264afec9785fe
ca655f5b3dfb03ad06491e4094a1066ec4c5ce0a
12904 F20101113_AAAOVU hirsh_a_Page_095.pro
4fd93009ece49c9bfc6d6a27176919d2
4038cce2a5c854588a08777e143e2486d72f22f9
8888 F20101113_AAAPCD hirsh_a_Page_081thm.jpg
03b6076c8cf36d13334f1fc8e102f6b0
44a592498ad96a7a83e5e07248c8514b73be4a67
2284 F20101113_AAAPBQ hirsh_a_Page_099thm.jpg
e82174c8283255f485e8df8ded0890f4
d01dcf3e1dc87ac17a5cd3785d05ee0c0681f75a
13399 F20101113_AAAOWK hirsh_a_Page_115.pro
9be8b8f57b84d995e03f3375dbf1d2f7
cd46b939f19a93167702a46f96d19e1440e03ecc
33884 F20101113_AAAOVV hirsh_a_Page_096.pro
2f2e647e290f328550232074cab11cfa
9aacfbf5d1348f3bdccea30e1a0cf867547674f3
36376 F20101113_AAAPCE hirsh_a_Page_027.QC.jpg
4a871f5c33f8bd9bb3b13ddadf458db6
1e6f4df817257e3684d641c0c079e31ebff7a308
35363 F20101113_AAAPBR hirsh_a_Page_034.QC.jpg
817169ed4309cb7fbf5cff06e4dc14f4
f5c31067a1347eb19cae255d84efaad8bc93e15a
548 F20101113_AAAOWL hirsh_a_Page_001.txt
bcf48e77d0e7a74c49eda3ea554f7763
bd1b30eb603cf196dcae5069c931a1ebd7ac16bd
11527 F20101113_AAAOVW hirsh_a_Page_097.pro
4814fbef270abfb6afaf264730218853
94c8f5d02da58c989c2898e5e53e762b780e5688
35611 F20101113_AAAPCF hirsh_a_Page_029.QC.jpg
da0816dc066a7f547cf85603ba789081
6dd0706276bd1a0047ab8c891c23fe02b84fa02b
2111 F20101113_AAAOXA hirsh_a_Page_021.txt
a87839d1b6e99934ae9aa6e8dd0bb9a4
e845a628f3267a06ba3b1a491d97bd92e7b94527
36062 F20101113_AAAPBS hirsh_a_Page_077.QC.jpg
7215c2d4d44a3f1125bd9e245f3e4a6b
d2aee3e7197e3c75bd18b189fc4ddc08d9871b11
1077 F20101113_AAAOWM hirsh_a_Page_003.txt
f157a016593b43bb1421136c199f1e5e
902f93440c3ca5398bc17820ba96382722f1ef7d
19750 F20101113_AAAOVX hirsh_a_Page_098.pro
8272e708e23efdcfe26761cd67849d67
fc7a9e87d65b87692eba83d0c52561bb6cc33ffd
8630 F20101113_AAAPCG hirsh_a_Page_039thm.jpg
5e57d0b126d3e6ab7140e391a8daee5d
e8c51603c59db7ecda18e9f642abf7aa622362e9
2199 F20101113_AAAOXB hirsh_a_Page_022.txt
6c95549a86a8f05643d3cdefd70b7e81
32e2d0aad5d73422b94bd6abe0d83022b41d15ae
8887 F20101113_AAAPBT hirsh_a_Page_103thm.jpg
7c16a1bd76b11952980565379b489020
7867bc22c04c72bc80049970a6ffed0d9fd67129
3401 F20101113_AAAOWN hirsh_a_Page_004.txt
35c19ba6589e3ce126da6fdc3fc50043
6dece3767453092c0fc9840e57d85c2a9059fdeb
24344 F20101113_AAAOVY hirsh_a_Page_100.pro
f583ec24d5842970fed718df2c619ca6
001e89bbf0570e2beb4b4db34defb41c82526908
8024 F20101113_AAAPCH hirsh_a_Page_035thm.jpg
26cbd028d4cc795f93d1ee0a89f443b1
c9e4fc4a614832817933498d3359b1d67d6427ee
2225 F20101113_AAAOXC hirsh_a_Page_023.txt
32e6237d55b3f863d92f533dbf16ade4
c30b866f074aa6c23f5c6d8e3f8727d2c5dbdb33
35688 F20101113_AAAPBU hirsh_a_Page_021.QC.jpg
0549a76a0c3e559362507424d36c0f58
bd66f54af899eec8abc4cc115e54bd3689313b2d
11449 F20101113_AAAOVZ hirsh_a_Page_101.pro
d32273c9c9f48734e2e22bd8242596d4
f259d7e9a44d8cbb2d28c1997d93a199463d3488
33969 F20101113_AAAPCI hirsh_a_Page_047.QC.jpg
0b4e03895a3c0a57d784d6f94153775c
0595bf027261b5e49e8eede92988db8041ae672c
2278 F20101113_AAAOXD hirsh_a_Page_025.txt
f9d50a1727b0fffc3006eb714bd69a18
93c17d34c3f19f7751faadcdbf2b4c4fcb56a442
36527 F20101113_AAAPBV hirsh_a_Page_083.QC.jpg
2f39d25c9d96eb967df4f8d39eaa0468
d5ee2db171899e8fa63bc30938707094c090f87b
3457 F20101113_AAAOWO hirsh_a_Page_005.txt
ec33563cf84eb8bdb5a4272e4dbe7057
136518a3634b82c10a5c79fd478bc39aafcfeea0
3381 F20101113_AAAPCJ hirsh_a_Page_055thm.jpg
fde4ddba8b734473426a7b7fa6fd4d8e
f293002872ebca4e7f26c029e7e492e808c85e2b
2203 F20101113_AAAOXE hirsh_a_Page_026.txt
4af1dd30f5db0716c6faeba9563abdca
f2cc22943a8f8aaa2c5616c6ff0e92c3625e4819
20986 F20101113_AAAPBW hirsh_a_Page_008.QC.jpg
ca30a97fc46c7674d1a44141dcc7ea2d
94d9a9c9292fe803ef0a74bf13fb95ffa5ca5396
1597 F20101113_AAAOWP hirsh_a_Page_006.txt
7537b3185934df89acc53df60f54262b
7e892a2982339517f0a50af3ca008e1fa014da47
23936 F20101113_AAAPCK hirsh_a_Page_096.QC.jpg
acb229138ebce946bdc7c3bea5f60d4a
2f8433158819ab3239cf3ca67c32cbbce2d23352
2176 F20101113_AAAOXF hirsh_a_Page_027.txt
4b244111672dfc5fa788ae6b22a5e46c
879e7c84d057888e6ea51074a77d9f657aabd513
35408 F20101113_AAAPBX hirsh_a_Page_033.QC.jpg
9495b6ffa9a6b2706eb03be5777f4a9f
f4ed9868b8cdba64762a1fdbe30666b037524dd0
2091 F20101113_AAAOWQ hirsh_a_Page_007.txt
7ec291313ed9c99c4bd9a128473c1a96
8dc085513bf8940b5d94081bb463da41d03e2ccf
30621 F20101113_AAAPCL hirsh_a_Page_064.QC.jpg
12bd85500e2978ee773029f8d434c9da
8bc7be30f7b0c944fa04a39854f6be4519073027
2118 F20101113_AAAOXG hirsh_a_Page_029.txt
7fd13dcfb751284c580b5b0f6373cb4d
2e37488bced8795d55e7644a93ca45cf371f1145
1216 F20101113_AAAOWR hirsh_a_Page_008.txt
b453b26bd96623409df3cf5665c5c60b
4b75ea8d71275c2addec914a7964e608189b27e2
33673 F20101113_AAAPDA hirsh_a_Page_062.QC.jpg
4fbaf9f42000f7f0394e5c733b6f158d
2c43841c6c558e1e97cfde2485bfb22057608324
8337 F20101113_AAAPCM hirsh_a_Page_097.QC.jpg
96e06039152effa228bddac235144d12
aa4bc18afe7e335491c564852865f39fab118755
2275 F20101113_AAAOXH hirsh_a_Page_030.txt
e3c43d9c92d96fa8f0f78c5eb18aed2d
5e27a51735f608b50701e1e0e1fffb5f96553c36
36600 F20101113_AAAPBY hirsh_a_Page_031.QC.jpg
fb74bab0ebde0055d2f9bfc92d75cae7
819c941445cac176d7d0fa44dec7871f21ff4b52
2071 F20101113_AAAOWS hirsh_a_Page_009.txt
e94e5d486de7f97f1b3c55d58300626d
67891e27f6f30f87e08e7c875941db2435953989
8126 F20101113_AAAPDB hirsh_a_Page_009thm.jpg
d7415ee11d90d565438b4b751b67c86b
0aef9732e2b60ea26da05314e005af3166d4bff9
8832 F20101113_AAAPCN hirsh_a_Page_070thm.jpg
ccd2563fd0cd5fea53dfc2671a593e62
a9371f9ae694ad7b2b6d3a26ab5c96132fac9079
955 F20101113_AAAOXI hirsh_a_Page_032.txt
9074f65cb437cbf9b76373c44ee563ea
93e548a0d32b4cbe2839642a28407be692127476
8411 F20101113_AAAPBZ hirsh_a_Page_101.QC.jpg
d511f0516b286caa900441e158c3935a
333b1e2d36178d608544c032d76052d9e0a05ae0
2302 F20101113_AAAOWT hirsh_a_Page_010.txt
6ca017f2fcf8bcb3736808f307026ed2
296067fefdcba2ff1de6cabd1170d7134e17b1f8
33237 F20101113_AAAPDC hirsh_a_Page_054.QC.jpg
cd52551ca9806118848fa4064af8e763
4e5774e30a264f9194eca707aac378922604f760
35657 F20101113_AAAPCO hirsh_a_Page_079.QC.jpg
b57a78746758d16affc85c540b86b734
c28544c9069e64e56974ccace8c8995e26685786
1939 F20101113_AAAOXJ hirsh_a_Page_035.txt
f312f42eb9ee01c5c2d6732454e4c918
705affced24108709668e03973350865c9493182
2202 F20101113_AAAOWU hirsh_a_Page_012.txt
0c5cbfe87cb734d62ed0784afc297416
61592da82ff311a8c168c74a97ea3be7d273cb51
35992 F20101113_AAAPDD hirsh_a_Page_014.QC.jpg
5e753055d71bf8384c19691a3bf59a95
30f18be9e66ef7862840bd7415b2b92ce4b79507
5714 F20101113_AAAPCP hirsh_a_Page_100thm.jpg
f03851b64744845388ad30f4256e8b7b
e218baedca377c50c27963a570c410b1676390d0
2251 F20101113_AAAOXK hirsh_a_Page_036.txt
8c00b6c2769c1ef8aa6b2dea0d7ced15
ea75373dab7d7a08f6bc46db450db33492293986
2190 F20101113_AAAOWV hirsh_a_Page_013.txt
09e2bff6a6b5ed6e92aca654a0d6be4e
96da397c29135efd195f5d80f6eb24d3ca672046
36327 F20101113_AAAPDE hirsh_a_Page_112.QC.jpg
69f776e3c6051d4250c73c17b8781813
35ecbf3dbbf2ea6f1bce93feef121f061d0cfefb
37118 F20101113_AAAPCQ hirsh_a_Page_071.QC.jpg
9d533ec67daa59a548ee7f5982d96be3
8188fe7aedfc51ec234e71c29b350440b4025a90
F20101113_AAAOXL hirsh_a_Page_038.txt
6719f21d89c40b39c7e0a3a6a7e3fbee
d9baacd974c943ad290308d4dc006af644cb5a97
2123 F20101113_AAAOWW hirsh_a_Page_014.txt
5a1dbe111aa410df3c957c5a8bc15d1d
7d1321d3d7aa38c012186c2f2053662126e4487b
35426 F20101113_AAAPDF hirsh_a_Page_070.QC.jpg
94fa25d6071dcc5d6ab29e68d1199429
b1bb3a1093e2d04a752c1e95b7399857463258cb
29830 F20101113_AAAPCR hirsh_a_Page_085.QC.jpg
d2e121d90ef480de3e30c212c0c5012f
d3d50de0fb28d11f35ee926e2bd79a7a9323b4f3
2087 F20101113_AAAOXM hirsh_a_Page_039.txt
924f28ee2dbc5c8f389129edab592693
6c87590228cae42622e2095e38345f00dec2bd1d
2187 F20101113_AAAOWX hirsh_a_Page_015.txt
80486ff1f875bd25af702933ebb43235
54081e6eb6790f06ccab5925d0ff76542ed065d3
34634 F20101113_AAAPDG hirsh_a_Page_050.QC.jpg
717334b00c365835689c65ab1469b987
cc35e86cef20be4a9be69045074684a14b9eca84
2170 F20101113_AAAOYA hirsh_a_Page_061.txt
a67637779e7ed37fb243c506aa27a1e8
46e68e3338bc857db5d745f78205a9aa8fa32f6b
20257 F20101113_AAAPCS hirsh_a_Page_114.QC.jpg
bd7cfa46dcf29440559fc8f553d5955e
dccbb87e756283c451ae6abce7deb5bb6153d9ca
1663 F20101113_AAAOXN hirsh_a_Page_040.txt
5c2869f5c4b201c621919e251640c2b8
e7f66a284b33dcd793367286b89df9cff5234f63
2179 F20101113_AAAOWY hirsh_a_Page_016.txt
aa2c5bf899a91ed16f5d7dfc5dc0f1d2
0153ec3a21a50e5e3237f16ea83f0fb9f993fad5
31755 F20101113_AAAPDH hirsh_a_Page_007.QC.jpg
4558cfe22cd73c77392dfd65ee93cbe3
ad4c4c9e7ef0c25f79b042ae68cdf7ab0ff078ce
2016 F20101113_AAAOYB hirsh_a_Page_062.txt
2c335a20e5bb45e4dc4a8e912b954236
cb2579bdd7806c9a88b0d477a024cf36f93b1a03
37451 F20101113_AAAPCT hirsh_a_Page_011.QC.jpg
8af9a3c9a431ca413608763f6bf7b0a3
a61dd5cf9d124fb3da0b575b36f2f96629e7c32a
1974 F20101113_AAAOXO hirsh_a_Page_041.txt
f1a4a0dcffc16ef9ca32f36d6721caef
0c432b1ae220803cbe3a099be95659dbd534385b
2120 F20101113_AAAOWZ hirsh_a_Page_019.txt
5f09546360fa4b53553f65129646a125
b730192fa10d9d63a492ea4ec23b62e4742b8fc5
9546 F20101113_AAAPDI hirsh_a_Page_113thm.jpg
76337979724d28b85b6da8a7104c42d0
f6615f280b1eb16837ab3e0f5b7f508fccb0c979
709 F20101113_AAAOYC hirsh_a_Page_063.txt
e8018140e2327087ce446e39b2df99c2
7b404fa14169c5be24fbd349b63453bb8657856d
9201 F20101113_AAAPCU hirsh_a_Page_071thm.jpg
29c82cdfbe5e33e59b45feec9c1200fd
f4144f13eb7d0663ad06741176ade012ced6e5a3
35235 F20101113_AAAPDJ hirsh_a_Page_074.QC.jpg
effcb033c823c1c1e19cfaa4a5eb4264
fc5837f9cf1908a8f5f9fb25929632c6e4071c95
1753 F20101113_AAAOYD hirsh_a_Page_064.txt
fdc4be5d222631bbacdca18029515024
5a9406ba8f11c7cd859735898d5e3182691188f8
36607 F20101113_AAAPCV hirsh_a_Page_068.QC.jpg
6803bcc16c423ae2362709169506fb7a
478b50a5ab5afb71a05cc2197452d6e283441abb
1973 F20101113_AAAOXP hirsh_a_Page_042.txt
8dcf95de9af28788d245cfb80aeeb031
053258183371ff26923aa9eafe55ade8a4d6a5eb
8825 F20101113_AAAPDK hirsh_a_Page_011thm.jpg
0181212adbcd04df514393b8318f3a6a
64de817d66b45adba2712990b3407eea3ca5e751
2099 F20101113_AAAOYE hirsh_a_Page_067.txt
dd2eac7c3f815162a3a3998ecaa8da94
7b33b009761db0373c5f142cf822707a05a9228a
38457 F20101113_AAAPCW hirsh_a_Page_030.QC.jpg
5627aad78c745d2d498abc1764a44f30
49161e8e05e65e80ef1e940fc74529a1c06f73f5
1379 F20101113_AAAOXQ hirsh_a_Page_045.txt
94bdf710dcbd397184ef9a1b0ab877e9
148bc8ee4d521fb71bfddb7304490f474e34721a
34622 F20101113_AAAPDL hirsh_a_Page_057.QC.jpg
97fed2732f3225249d39a4c27a3ac0b0
b65a08cd9616b361534d4859571880eac5ee248a
2218 F20101113_AAAOYF hirsh_a_Page_068.txt
1c0558cf8403ffcf786c448c371e15b6
fa40c78548bc51184582b3ec1b71db22bf007b8c
35539 F20101113_AAAPCX hirsh_a_Page_075.QC.jpg
6df6f30ebd074f82cebd806d70296ef7
df820fe18156bedb229f9b07a08f02b8ea386767
1972 F20101113_AAAOXR hirsh_a_Page_046.txt
17fd5cc10f1ea25b15d39c9b3df9a427
aadd610ddf42ac0290e5bf085f496a7933926e45
35580 F20101113_AAAPEA hirsh_a_Page_018.QC.jpg
e505a32ba1b96280e142ad8d4885575c
b9868d953a8d23fbb797ed7d9253d3aeacf64c7d
37352 F20101113_AAAPDM hirsh_a_Page_023.QC.jpg
79b310ff1a85b50b45c968034c687fd5
27e4fc27435b06f9798daf46a862a29a257b09f4
2152 F20101113_AAAOYG hirsh_a_Page_070.txt
a9a6393beb8130c44b48673d65d2e4de
e8bedf627aac0c54e89c0bf88748dfeda4facee7
37648 F20101113_AAAPCY hirsh_a_Page_069.QC.jpg
ad18a31111d057572bbb91b7590ad282
93a9456432d2128b8d6eccd16a7e1dbafeae8b1a
35335 F20101113_AAAPEB hirsh_a_Page_019.QC.jpg
74d28f359ed2468227d9d855d414597d
568ac10f547196b4f33fdad2accf5e61f858c04d
F20101113_AAAPDN hirsh_a_Page_030thm.jpg
97a93d97af5e537154ff1f7339fab0ca
c912b562c3070bc80ed2b503998a9d69b12d51db
2220 F20101113_AAAOYH hirsh_a_Page_071.txt
385508da04808c82aaeabd1710dcb14b
86d4c604e77ccf954015efa2a8da6dcdc6fde0f9
2069 F20101113_AAAOXS hirsh_a_Page_047.txt
bb65fe0ae35ca7872cd785b85016e5fb
c99bdff055a7deab981e9d6d2b1091329cd5fc75
38005 F20101113_AAAPEC hirsh_a_Page_020.QC.jpg
7e1c54933d5d1576c46587667c203ae4
0aad3c4dfb24c210ff25990b28752f4ef5968d17
8382 F20101113_AAAPDO hirsh_a_Page_062thm.jpg
0638da198345d34713137ad7674d0247
d5040a133251d81fe840edb383eb6d6aac884238
2243 F20101113_AAAOYI hirsh_a_Page_072.txt
7b41f195b8f69fc344dc3ef4e23e1d61
83fc8517fea4480e04ac7f3f60899b3da01a373e
8932 F20101113_AAAPCZ hirsh_a_Page_051thm.jpg
ac1664a68e4c8324645d501324abcd61
d628c1979050c620bd09dc69e2f80e9eef3891c4
2171 F20101113_AAAOXT hirsh_a_Page_048.txt
50189aefe23e7893070e59fe835b0232
45aa8757fd2a2602cb3c88cfa013dde5e11a19b9
8815 F20101113_AAAPED hirsh_a_Page_022thm.jpg
16c1a378c85f270b3b2eff6b0e36e5ef
e1cd50c5a76179627e22cbcc4b7ebf91870b623d
172132 F20101113_AAAPDP UFE0021521_00001.xml
940c872971c4da0d3bfde242d0f89de7
955447976522d4d8eb80bc86273f5a945ab90c56
2237 F20101113_AAAOYJ hirsh_a_Page_073.txt
e7000db4d4edfb3d8f596080e30976ea
b57f5a1297e47679652de8294d2a0516098d3f4b
2079 F20101113_AAAOXU hirsh_a_Page_050.txt
c726e9a48fe2062e01e74cd9ddda7795
a3ab661127ff8cb16b414fa31ef263f2ff55971a
36583 F20101113_AAAPEE hirsh_a_Page_022.QC.jpg
92e6d39f515a1f7d82705d3cdb188734
ccc61fa49591f594130702521765f5749de5ca3c
4584 F20101113_AAAPDQ hirsh_a_Page_003thm.jpg
6f6100963d03d67a879b99a1e750682f
7d48f445798f5783e63c022398c407e0cf1cb8ca
2150 F20101113_AAAOYK hirsh_a_Page_074.txt
cd3d54ef645364da7097e889672d38ef
5bb1a0b45118e00b9205b5a52c7b3a7d640f0d4a
2255 F20101113_AAAOXV hirsh_a_Page_051.txt
8ff76d699280d9c0d821722e4d2d6a63
68c267b46884b9ec9aedfc43fbdb9926e128d9ca
9033 F20101113_AAAPEF hirsh_a_Page_023thm.jpg
491cc77497725fc8e8ca73b905078b73
435de4fa68b4db8942b04b68dab6734615d0e7d1
27451 F20101113_AAAPDR hirsh_a_Page_005.QC.jpg
ef1495a4ad0e888f07381627fa2fc679
c4e375452cc86b2eb7b66619362f05545ea667c1
F20101113_AAAOYL hirsh_a_Page_077.txt
000008d4872ff23ec34f1b20e607dd7e
c8efcd7c539a5f03e649380640773a37dd2e6ed7
2601 F20101113_AAAOXW hirsh_a_Page_053.txt
644503c000b94c424d51c1f96c34ce06
a44fdb7ff2c208e4996484f2d4e2641b47544a5b
36210 F20101113_AAAPEG hirsh_a_Page_024.QC.jpg
5f895acd3160ade7cdb578ea3a7747ef
bfb2767ba021ed7ef7e76baae97fc08b83c93f04
517 F20101113_AAAOZA hirsh_a_Page_099.txt
7521555fc9c9aa3a04afdbadba353b54
df063994137fe4f0d055d4705c3ab63ee04824af
4990 F20101113_AAAPDS hirsh_a_Page_008thm.jpg
ee62eb9fb9860d148b57e32132c21727
c09725995adbed3541387c47977045b7999fd0b8
2235 F20101113_AAAOYM hirsh_a_Page_078.txt
0d41950b990545b099071e84c7d11dc6
18b778f566f85409b4f8b961931a3abe5ef7209e
2252 F20101113_AAAOXX hirsh_a_Page_056.txt
a8df03537eb50e15b1fe2952bf731de3
f61787e98c0d4ad52adb3aae7dc5e7fdbb7e1133
9074 F20101113_AAAPEH hirsh_a_Page_025thm.jpg
122e849bc0b9ccd90a0c31e9de98951f
0bf6837608c0c471380240940d39a0bd43c66fad
1119 F20101113_AAAOZB hirsh_a_Page_100.txt
b4aab2dd9ddfbc7d7db9c2b94c9055ea
1f6ecd36dbbb2b2a2a7e4372ef8d2ce757011e8a
37341 F20101113_AAAPDT hirsh_a_Page_010.QC.jpg
a06d81dcb66a080c9ac6ac61f135fabe
19fc1596332044e5a5834ed03ec7fb3c71110c7d
2188 F20101113_AAAOYN hirsh_a_Page_081.txt
f888a965bfa29ea1b3823cc3690a5b47
e3df8440444aaf495cd4f33f5c3c21b7c2d889ff
2065 F20101113_AAAOXY hirsh_a_Page_058.txt
fb93aa55081308ebc9d26ae6795f108a
76043e2d5c16c80bb2d87d11ccaa2726bbc4be70
37559 F20101113_AAAPEI hirsh_a_Page_025.QC.jpg
1dde06afaac83ecd81e5f02b8e0280a7
0e4c1d7e837b996afbff296cf49c79dadf1645f8
454 F20101113_AAAOZC hirsh_a_Page_101.txt
4708ab7204ffd05606796a4ddae665eb
591c9d590ec7b3202d80b7f988a25fd07624db0d
36377 F20101113_AAAPDU hirsh_a_Page_012.QC.jpg
c9a1833b00147398d73ec3c303a06a3c
5d0a83b9579c630371d8410efe1b100367f353d9
2172 F20101113_AAAOYO hirsh_a_Page_082.txt
4e73d1c42ad3e8b3d3669976dacd8139
7382439e620c4487b66bef4c11a24a0d203696d9
1885 F20101113_AAAOXZ hirsh_a_Page_060.txt
b7c5391a9c2d4652cf155eddc3b5d8a4
497d18c135066386db7c602e2a369e7a12c150c5
8783 F20101113_AAAPEJ hirsh_a_Page_026thm.jpg
d882a98f009793e7ff71b5fa6705dd3d
88fc1c2941e6c81e48b3f1ae28f4cc59720ef66e
2589 F20101113_AAAOZD hirsh_a_Page_102.txt
0829bc548d9ea84c36476a4bc1051c5c
5f5777883bf0850f336b48cfd0952e607959d1dc
37117 F20101113_AAAPDV hirsh_a_Page_015.QC.jpg
2b6e8daff13a939676ebe627e5dd8559
97b13f5d63efcf3c0c1c0d1a625f7b0cfba1edd7
1785 F20101113_AAAOYP hirsh_a_Page_085.txt
8b9850cd73fefae5ca0ec008602598d3
995b64b15351a169ad522b4a2695b9dc5863fbe7
36309 F20101113_AAAPEK hirsh_a_Page_026.QC.jpg
116e6f4a53623917fec2e7b52e13b309
d24083afb7ff46c3aede3f61fbaa3519135b85f8
2721 F20101113_AAAOZE hirsh_a_Page_103.txt
3bb9d8344ea41889ec995dd13c8574d3
b0aa8903e7d2af5f55966ad9a44ba39a9c613b49
8894 F20101113_AAAPDW hirsh_a_Page_016thm.jpg
dc52a7607cd5046b0cc436e2ab32cded
9175aef0317709d21363c1a55af2eb1f9700cf89
35594 F20101113_AAAPEL hirsh_a_Page_028.QC.jpg
6e8137db68535f8c43eb411bc3299105
7e63d11d5246c00d2bf922f0ce972dba20d8aa58
2634 F20101113_AAAOZF hirsh_a_Page_106.txt
efcdc97e2589b12b1bd48f731be88473
2f4a717ca96010f69a3a806fc4393b034f272694
8569 F20101113_AAAPDX hirsh_a_Page_017thm.jpg
ef9783f7ce22c4bd0a365766e803ab42
05a4ab54fb40fe855fb7f18a2ece0308753ccd22
1543 F20101113_AAAOYQ hirsh_a_Page_086.txt
f76133290078f8b298551b5dbd35f4fa
4e7a85e637975769ef8db74cea83f9bbe78e92d2
8936 F20101113_AAAPEM hirsh_a_Page_029thm.jpg
ba1588b7e96b39369784da59dd517ea6
a200e643a63dc43c1961c1f7a6ed754509bf80e2
2508 F20101113_AAAOZG hirsh_a_Page_107.txt
42a7f5842d21b5d7c228efddbb9b6b87
bcc22c7e53aeeb14ff1c35f8e91d97003f10497f
34453 F20101113_AAAPDY hirsh_a_Page_017.QC.jpg
086cef707434885bd6047c9dcbd817ce
fd9f00f8e14411e6a5127c7438df04a461acf6aa
1719 F20101113_AAAOYR hirsh_a_Page_088.txt
26a5b99c8afcc668fd6f5642fd2d722a
ccbbb9e6374fb135dd3a32a53a23219a24190bc7
12384 F20101113_AAAPFA hirsh_a_Page_052.QC.jpg
1276136756d7695fd0fff175a79239b5
a100ab902866a5662a30979fbc1790905e3995ca
8862 F20101113_AAAPEN hirsh_a_Page_031thm.jpg
2b989249fff00ed73decda62a1e203da
5269b6337beb80dd0b0553b62cb2006c2ba0c051
2680 F20101113_AAAOZH hirsh_a_Page_108.txt
e0a2f6674ed19f7194c869e858857674
9b6a9c89b21064deaab8929d54a9d4545bf38e00
F20101113_AAAPDZ hirsh_a_Page_018thm.jpg
e5add5e537a1eb51cd8320dd0f904a02
dc9eb20f19e45801ffad9f59a35fb46caaad2904
525 F20101113_AAAOYS hirsh_a_Page_089.txt
bb3b93db2cda8f417ac988820d098b59
13124be70fcbeb5be4ad999bf953759551671c83
35092 F20101113_AAAPFB hirsh_a_Page_053.QC.jpg
b28b5ba7855a82459dccdd11e7123324
c1a47e6682f62e9f54e99b9805f932ad05b9860d
32357 F20101113_AAAOBL hirsh_a_Page_035.QC.jpg
f32df45ee878001b4b245509392ed51e
9c069ebcaf93ca1a49e4f221e91412d2dad02305
4182 F20101113_AAAPEO hirsh_a_Page_032thm.jpg
32420ef23e40f7fee1a6cc80012d38bb
b47e6a880bb1aac43b31964a5c2b55cae839203d
2552 F20101113_AAAOZI hirsh_a_Page_109.txt
c78e1d85a4e7607fe1ac606c99ea6068
4e83694480a0661206a320e47c9c2f34534f263e
1467 F20101113_AAAOYT hirsh_a_Page_090.txt
63f3146df784eaa442a5f3c0b4ab98b5
173da9824e903fa44ce98d7c6318cdc76bb8d8ca
121165 F20101113_AAAOCA hirsh_a_Page_083.jp2
1e063c329d76ba60b2a4d1a87a93e8b6
2dbcbcfbef995594699112a17f2c5cf0e7511e46
13412 F20101113_AAAPFC hirsh_a_Page_055.QC.jpg
6f6508075777f2319937e5354522f1a5
45530145e04c2d3a983e6833e5b3457a4af4e8c1
85955 F20101113_AAAOBM hirsh_a_Page_088.jp2
9d04d2322faf2be94cac81e4dd1d87db
8425174f6d8588b1c507a9fe650de282486057f2
8496 F20101113_AAAPEP hirsh_a_Page_033thm.jpg
23c8efa053c5135f2566ffb622863430
3981d66d4446dcf0f962b8ed32ed2eb3f44d5f1e
2790 F20101113_AAAOZJ hirsh_a_Page_110.txt
d1ca7168ab863ca628294dfb50bf305f
24018afd44b059f3119b585ab3ff35098392c55a
F20101113_AAAOYU hirsh_a_Page_091.txt
d0a166cbdd57be8284216842a96586e8
6a8e339886fb2acaa075e4fda96a368847ede204
110083 F20101113_AAAOCB hirsh_a_Page_049.jp2
08c814aaa411aff027e96f2b661520c9
79a8dd2344f7056f8e0135cdf72ce32511ab2fe9
8266 F20101113_AAAPFD hirsh_a_Page_059thm.jpg
13857b17734df1ebc337953a1b9f69b1
7f766d06ef7853e76b5122c1bd0aab1d029e86c7
8738 F20101113_AAAOBN hirsh_a_Page_015thm.jpg
d7160507d84442b1a75ce2e4ce94aa82
fc115e15f08bf8a4810825c919271a692c72f9d2
2548 F20101113_AAAOZK hirsh_a_Page_111.txt
c544171545d9c63bdb9edcdbccd02ddb
6f710f5666f78f83cb03d72bc2746d650ed937f0
527 F20101113_AAAOYV hirsh_a_Page_093.txt
d0fa7a9e94a3677076e509d12db17087
945923ae30a2e407c5429ed3f3db5ee2a6045ed1
27203 F20101113_AAAOCC hirsh_a_Page_097.jpg
8ed0fe647ab0443152921dec01a280ab
72d808310491b9324cb3398d2d9bb15d29b9c9fb
7602 F20101113_AAAPFE hirsh_a_Page_060thm.jpg
274d2075ffe31c531597722522ef1032
1d02cba5fbd494fe69097e8505d9fd5e7eb7381a
114220 F20101113_AAAOBO hirsh_a_Page_033.jp2
8b217f2a2f1b3a3ad16d9b5b0e62cebf
cbd29ae3b0213246043a8008dfd3fc1a6c1c57a6
8796 F20101113_AAAPEQ hirsh_a_Page_036thm.jpg
36e74374e2e959fec0c243e9e477d9e0
c229a37688d8340466c7e3589982a49a7fdc531c
2648 F20101113_AAAOZL hirsh_a_Page_112.txt
79f7baca752927436ed593fd07f832ed
9858f73b6e16e1e89e57c033fee0cb688a016df7
1066 F20101113_AAAOYW hirsh_a_Page_094.txt
0104e3c639f1a7472c95b31591bb8c25
89d705a9169be936325275c0c646835c353ddd01
32239 F20101113_AAAOCD hirsh_a_Page_115.jpg
b1dc1e7e8b65527ed866dcb2d24e9361
b37ab8a56a4b741413bdba059b966c94472e0605
30418 F20101113_AAAPFF hirsh_a_Page_060.QC.jpg
b6dfdb499ea69377bebee4afa496ba58
6d4dc451608f37624f0fbae13d69ae8f91a84781
37214 F20101113_AAAOBP hirsh_a_Page_103.QC.jpg
ab370d420e1ca9650240ca693cc36139
e05eadd6684ca6497cf268a55c647839f9562928
8986 F20101113_AAAPER hirsh_a_Page_037thm.jpg
0c7370a6d8915f50d5c886b879734363
31420d00387fa1a0625f3df71ec7d14324b8e967
2691 F20101113_AAAOZM hirsh_a_Page_113.txt
c084176de08e0b0bbfdc62dabaa0d757
24b2a84b0bc62a631a54313e167c3f7767012ad4
1547 F20101113_AAAOYX hirsh_a_Page_096.txt
dc1e188ea597433ffd4d95f627acb8d8
89ee9bfdf3e1354452b7439220604cc529075880
49374 F20101113_AAAOCE hirsh_a_Page_041.pro
1623a87f05020dd4b25629db5f5c90fa
596330ab2a42ed872fac2c9e30656ad70669529b
36950 F20101113_AAAPFG hirsh_a_Page_061.QC.jpg
6f4805adf4f6e20565bd85b5c6f6e7d6
4962c03ce899996cb7d435fb68f8b50c0e09987f
8199 F20101113_AAAOBQ hirsh_a_Page_046thm.jpg
09d64878fccebee3a76c7e6585bcda70
c3d38f4bb9fc9d5724401bbc95bab440115ad576
35909 F20101113_AAAPES hirsh_a_Page_037.QC.jpg
efc2589d22e2956c4f0ec895c8e6c54c
4f8cc4945451d04eb09aef90140d3913d360090a
1343 F20101113_AAAOZN hirsh_a_Page_114.txt
58aa4083430f8c54f9436edefe382520
09424ead869cf71d9dfef338dfdd41f63c5989f0
457 F20101113_AAAOYY hirsh_a_Page_097.txt
c8ed9e01984a90ef1c2791ee70baa9e0
a0507da7b18b39caa45e25fb330deb9a886c06fa
6000 F20101113_AAAOCF hirsh_a_Page_096thm.jpg
c7878ed41f9199a558e057567cf65b09
c5d25ac006746e97483a3b394b5e9486c477549c
2558 F20101113_AAAPFH hirsh_a_Page_063thm.jpg
f1f166b6b82164807d8cfaf5ab843762
e0fd7815afe2909cc18c10f9b08fb5969c9c559d
7244 F20101113_AAAOBR hirsh_a_Page_044thm.jpg
b6e4d0d2a56d406e337baf92a9d60d9c
82725d1d44d371070e7136596e86bf2487587124
36775 F20101113_AAAPET hirsh_a_Page_038.QC.jpg
8ac27531d1fb640072ae9b64624c29e8
f9999ccc86ba4991ba8ae94c4a979e11bbe90be2
573 F20101113_AAAOZO hirsh_a_Page_115.txt
f23996d69ab5edc6ba194622f257462d
c2d3ec7e02bb1118449eb7a8e117b4639b08a2af
883 F20101113_AAAOYZ hirsh_a_Page_098.txt
379f7c017c60cb95f13e51010d84eff5
74bb3120f53730a811095f605ca100ff7296068e
2195 F20101113_AAAOCG hirsh_a_Page_080.txt
46eb257a8e9092e86cf62737a9fddfc5
738769719401ce6fe5c98893d99d498525bc33f7
8449 F20101113_AAAPFI hirsh_a_Page_063.QC.jpg
5d0df72bb9a5048d4030160ea6d249e9
682d63bb1eac075e1585922277f0f1dd6ac1de61
36759 F20101113_AAAOBS hirsh_a_Page_016.QC.jpg
c8507a38eb83406626de910a9afb2092
69c5a9391bb3449e3b2e7ca1622079d19b405557
8226 F20101113_AAAPEU hirsh_a_Page_041thm.jpg
412b7182675e3c6d7f68b197581789ad
2e0f220b4c29eedb6bee8b4c63db07b1b10b4d54
2177 F20101113_AAAOZP hirsh_a_Page_001thm.jpg
50e4c7b3c3ec6594b5e0bc4480dbcea5
7b17b9bc7f92b0c9ab8d9abc17465a078888f0ae
2110 F20101113_AAAOCH hirsh_a_Page_018.txt
0348ce924bbfbbb85345a63ddd66126c
2dbc4b9e6d0d31d7ad2929199805df170f6d2749
8659 F20101113_AAAPFJ hirsh_a_Page_066thm.jpg
b5d981180364002dd83419131b3fcc64
3f746d89c06bdd0ef0d91937e310dfc3e09610f9
54799 F20101113_AAAOBT hirsh_a_Page_038.pro
dcc4816b6ba479a7ddf9e4c8ff353c34
1ee71ebaac8c2ef7a0a13d48552acda20a6e6dda
8168 F20101113_AAAPEV hirsh_a_Page_042thm.jpg
b6ea665f7eddedd8ac3df7a144e49b93
c08eb8ed5872d481be31671180ac7f0ea02c2a56
416521 F20101113_AAAOZQ hirsh_a.pdf
0341435f53fb482854d5f2c67f1304fb
10714019df8f64e53a25bd34a05348e9306ffc88
F20101113_AAAOCI hirsh_a_Page_038.tif
fcd027c8d7b7277bd8df3a377dd5073e
abba51ee0098807a6c207b649c71b7264618393b
33540 F20101113_AAAPFK hirsh_a_Page_067.QC.jpg
8b8d6e2ff6b71ed8804bee9212599332
5ea98c651ac645b35af6adb2b8e13a7f58a88bc8
80974 F20101113_AAAOBU hirsh_a_Page_100.jp2
e16f32cd3b20b342dc7c1eba6986661e
52ad0228e74f81899e78caf3403a0ccf92fcd634
11069 F20101113_AAAPEW hirsh_a_Page_043.QC.jpg
431dbad023a7a2f9cf88e1e4deb77f07
02fdaf50c8c77ee64363ec21b079b8632c4025b3
54447 F20101113_AAAOCJ hirsh_a_Page_028.pro
42dee9d75f7be10b4c8409db173079ad
007a3a864d33eff41df98a903f5770cb4ea0746b
37107 F20101113_AAAPFL hirsh_a_Page_073.QC.jpg
e9430c8f5a63c3f45dd2ab3adba140f6
edc2be8d94cd50d3588089fe89c09621e5a8eb00
F20101113_AAAOBV hirsh_a_Page_104.tif
45842e03d863b077815a57fa5aec5308
0a8ca487bfbbf9bd4b486fcb0958a97dcd4193f9
30219 F20101113_AAAPEX hirsh_a_Page_044.QC.jpg
bdfd5da907b71936fb9fee9e4143e48a
b13d4ac56ff1481bd0ac266cf96c4be9168522eb
8879 F20101113_AAAPGA hirsh_a_Page_093.QC.jpg
35f7d0a253fa31114b68179f1b6c4f52
9b1e77951434ec1ed764686d3e56f7c076c2f411
9060 F20101113_AAAOZR hirsh_a_Page_073thm.jpg
f84c79f317150ead89ee96cf7d68a79f
05adb109a59b5b9764f2bef793f4019d26c2f1bb
3210 F20101113_AAAOCK hirsh_a_Page_052thm.jpg
02a9ea3fcb688a162b0f5b64ecb6d33a
432ffb1e3061204a04ce64031ae282226f42a0fb
8919 F20101113_AAAPFM hirsh_a_Page_076thm.jpg
e9041803a655eda60cace9d2cbd8703d
2fea8aecc9246025eb0982b92dd8367426e6a01c
39201 F20101113_AAAOBW hirsh_a_Page_110.QC.jpg
ab71124cd364368682cf461b296d1856
b7d0c65d0538bfbd69753efdf80e908a0e45ff22
8474 F20101113_AAAPEY hirsh_a_Page_048thm.jpg
bd0629321592e31e4289cd2a077e2873
35f28fd6175ec9d5fd3b04ceefed3e306b442681
23481 F20101113_AAAPGB hirsh_a_Page_094.QC.jpg
6a10c9755583072c4e77b3b1e719edb4
a38b626be77f416a3a64b5c3f0ce038d3dbf0677
8940 F20101113_AAAOZS hirsh_a_Page_012thm.jpg
dcc1d234d6ac12f7fa042a7e671e3321
9589a9d151a8295f124d1a246f45d6b67143da54
97883 F20101113_AAAOCL hirsh_a_Page_060.jpg
95e9d5a4dc44cff1048963611671ce8e
b02454fa191092949196286c41215dd4f1c9a54c
8947 F20101113_AAAPFN hirsh_a_Page_077thm.jpg
fb261c1f1197f30ea1567f8a7bd5702c
4da0cc8e2630765d5eed59ab22de07b8219e7a6f
53603 F20101113_AAAOBX hirsh_a_Page_032.jp2
b77b667ff587284408f938ab215aeb9f
6092ebbe2ba2b87f4caebe427961cb5cd9aa32a1
33694 F20101113_AAAPEZ hirsh_a_Page_049.QC.jpg
029ad98a2877e9608dae4b832b739719
8ce309eec9afc9d2a639f55a9193bd1a3bbddeb3
2307 F20101113_AAAPGC hirsh_a_Page_097thm.jpg
961628d207cc878009999f22adae2b70
b4c722421fbda26df31ea00a11b48fca3886a3da
9675 F20101113_AAAOZT hirsh_a_Page_110thm.jpg
1e7ed20f2307fdca9a2623c103e45305
d48d5b82a88796f57cbba8d822bc2de103d5d957
34045 F20101113_AAAODA hirsh_a_Page_039.QC.jpg
feb99c6c1b01e3fe43601a5493908fa8
b60c637e5de79004ccdeaa17b297f48dcfdd9c92
127958 F20101113_AAAOCM hirsh_a_Page_109.jpg
c40cd779704e5e903d3f773656f898e9
5c662a94688896a3b000fe959cdb7552ef538502
36957 F20101113_AAAPFO hirsh_a_Page_078.QC.jpg
eb0d304b56e48b58d19e5cf76ca51279
e18d8b5a382fda77e9e8d7af3d0e9e673b666619
F20101113_AAAOBY hirsh_a_Page_076.txt
7c15834b06ed246de20e12a6626afe9e
353b913b49d7551beee0c7693fa806017831aa08
5765 F20101113_AAAPGD hirsh_a_Page_098thm.jpg
df46dd3cce794bd0fe0a748a055f2394
5446c1de8ac3765556bfc07b5e5c853a96dc7e0a
33554 F20101113_AAAOZU hirsh_a_Page_059.QC.jpg
f79fef3654f27623ca2b488c538f92b9
86b8ee0ab5a8ab914503cc8c7080a4e00f09fc40
122741 F20101113_AAAODB hirsh_a_Page_030.jp2
26630e1b941f412a16ad7991562c9efe
fe6ca7f3e8da132a90a852f47ac5232581814dde
578 F20101113_AAAOCN hirsh_a_Page_043.txt
744d1282d287af8d5ada21035461c4f2
23a27a88dbeff2a56b0bddb941cfb8e02c9b3a3f
8824 F20101113_AAAPFP hirsh_a_Page_079thm.jpg
a7d00e5cf1a29f2385f7d736e69b0047
50e8fd0913cfa0007f11644419688bd4f1aea15f
8650 F20101113_AAAPGE hirsh_a_Page_099.QC.jpg
4b0b798d599fbe30befef6404e9a87e3
fa3cc822b2b01fefd25c51d9da45882133bab82c
37170 F20101113_AAAOZV hirsh_a_Page_036.QC.jpg
680ef2a425d357dfb76199ed0cb98fb4
2aeca9a6019d206c0e569241cb00e5c2ac177f2c
33667 F20101113_AAAODC hirsh_a_Page_087.jpg
c56179cf4a9dd0764a6043e421829401
1df78744d567f0d071dc788d2dea6bf916892806
2224 F20101113_AAAOCO hirsh_a_Page_033.txt
960ac926ddd065a8c29f9cf9f4f94794
1bfc141c7125e8b0eddc61121e7191e50554ebf0
9069 F20101113_AAAPFQ hirsh_a_Page_080thm.jpg
2d95884bd5d9edba186c1a651c6b42c1
b289982aa09262fafe2acd641bf50776c6e54a2c
109666 F20101113_AAAOBZ hirsh_a_Page_048.jpg
09c27e9d5fca3688b5c53aed2dcd9a3f
d8ff41e75d294c40c5e8cb6e1a8aed94b92453a6
23123 F20101113_AAAPGF hirsh_a_Page_100.QC.jpg
92f413d38edba8851d0d811ce9e5ceee
80232add92a6d2f32f070d01c7b61e00201c1232
35535 F20101113_AAAPFR hirsh_a_Page_081.QC.jpg
a7a593ac5d0f7c6dab3385cdfd6c252a
98f3178933dc974045bc30c6b791e827cb576e3d
16531 F20101113_AAAOZW hirsh_a_Page_032.QC.jpg
21c7ac6477365df98d1c086fea3d87fb
5eee4085d185d17fccda32d12999730d929eecd6
F20101113_AAAODD hirsh_a_Page_024thm.jpg
3d4f644a44ac2448df8998d60d5d1c8d
3470014c6204e068e5e3fa0f8d588d0c909cf852
2178 F20101113_AAAOCP hirsh_a_Page_037.txt
6dea76125a1dee09c38bc7697eac6aa9
768f0f64708f2b7bc0ae70a0e24b4d2e4009ce8c
2276 F20101113_AAAPGG hirsh_a_Page_101thm.jpg
ddcdc61fb9dcb63f7c978b8ce084a6cd
ea2402636e25841979a83a631fcc892e1c11144b
9058 F20101113_AAAPFS hirsh_a_Page_082thm.jpg
8adcf61c1ef01b85db1fb0397b82eb72
3ff0c07d48284dd0f406e2747f32746a078442cc
9057 F20101113_AAAOZX hirsh_a_Page_104thm.jpg
6bdddfdb1b8ed331d5f9ad6b5a4b5142
cce62160b6b66c6be335eae85ad619236d8e5fbe
F20101113_AAAODE hirsh_a_Page_013.tif
311ae68d89afefb2d550318f2113f82b
f831f9012448a84587bda888edbccc4f2315ba5f
9170 F20101113_AAAOCQ hirsh_a_Page_078thm.jpg
a93c6b9d1de2738bdc2788d1b95c33b4
a933e700b109aaf895d0d9537394d4bd0cb93907
36708 F20101113_AAAPGH hirsh_a_Page_102.QC.jpg
33cc3521ce46aeae129508b74d355299
490fa29ee25ad1889e0dfc58cab5150f653c8d95
36286 F20101113_AAAPFT hirsh_a_Page_084.QC.jpg
860e225e707d114891880a79df15e2a2
f198f90d7e2620ad66a46920f14aa67b1b84fb91
9133 F20101113_AAAOZY hirsh_a_Page_056thm.jpg
17bb8e5d0d54dd5d52f6a1ce71aab578
1abda65b116b968c7e816fabd342da27292c7951
24491 F20101113_AAAODF hirsh_a_Page_090.QC.jpg
5e4df5f9a6a86802d85e0502c1e3db24
0d798a75889875f466ee32ff1b0c67891bc7bcf5
36712 F20101113_AAAOCR hirsh_a_Page_080.QC.jpg
06189fe06da2e389806a25a18703b6b4
375c5e26b2ac8e9110415e86fa89a3e8547a1ca5
9328 F20101113_AAAPGI hirsh_a_Page_106thm.jpg
e02594a75227f6866899253d49b0b4d9
565d13673c525ede4a9ee1a4852dd81e39404905
6315 F20101113_AAAPFU hirsh_a_Page_086thm.jpg
86019e914f17a0ca4f8dea5b4058b58a
21086ade89433361ea833d1518f2ed91b7206ae2
10064 F20101113_AAAOZZ hirsh_a_Page_087.QC.jpg
11cff1a0d45d9c929872024d97be1b89
f71f7076187cbff6ec9474b615ae55748a7660f0
55967 F20101113_AAAODG hirsh_a_Page_013.pro
ece6601028f9b9ca7a0e367f7262e11c
6ae50a46fe0d0652ca98fc468900f6e846115113
28208 F20101113_AAAOCS hirsh_a_Page_089.jp2
8958dc68ff228b7cd6cd7f001c9745d7
14e9bb1d7465f69969c87fd9e22a02753d7a84f4
37197 F20101113_AAAPGJ hirsh_a_Page_106.QC.jpg
8b75165e5cb8c7ee0dbb91302afa1947
1b9279bb0f96cca16dfe3658bbfc283a89d133fe
23787 F20101113_AAAPFV hirsh_a_Page_086.QC.jpg
d4dd99b924290bb8be4c2c1bf0c9e5fb
a12b4608c120cc1214d3b0f7cf31116b9241db4c
35204 F20101113_AAAODH hirsh_a_Page_065.QC.jpg
ffc3d6ada315c60a6b674e1ee06cdf96
637d883e54873c9d981d9c245efd2a88b0abea4c
2082 F20101113_AAAOCT hirsh_a_Page_057.txt
1f7243b55e17a336c17f69a9e1a73445
b70b880af626ff93a56046fc9f4e928683c7f4a7
8773 F20101113_AAAPGK hirsh_a_Page_107thm.jpg
43d47efaac7657d9e82c5fbdfaddc631
b28c564933fb65b2dd5c9557960bbbb9fced7c12
6179 F20101113_AAAPFW hirsh_a_Page_088thm.jpg
acde1962491966fd5e55050e6b26a858
a61b8a40b01a80bf0dc47d89c96c8119e12f05b4
112140 F20101113_AAAODI hirsh_a_Page_011.jpg
466b98032e088b7d35f8e0a3cb7bce73
b5b040babff31953575027a9976bccbf4626b255
F20101113_AAAOCU hirsh_a_Page_058.tif
6b20d1c673640575c4b4a441451fd60c
8cc05271e64be069010af8b17f40ab0398040e80
36238 F20101113_AAAPGL hirsh_a_Page_108.QC.jpg
a0cd268ddc1410ca9a583d949f23fd5b
28d5fcec39ccc1654bc2d9a5708efc0b7a8e84c6
8853 F20101113_AAAPFX hirsh_a_Page_089.QC.jpg
329d45867deab0a245fadafe9b69c0fd
9c22941598661b710ff17a924936462fe3270f4d
32486 F20101113_AAAODJ hirsh_a_Page_086.pro
d2157cfba40fbd1c623c87bea9803e5f
49b0e8a59b66bfcf6d542defac49e4b984560da6
121965 F20101113_AAAOCV hirsh_a_Page_056.jp2
2c400b8240d867fcd10483406debeb0a
1e883d808a867cb05173cdcc362315ed2f554bd2
9119 F20101113_AAAPGM hirsh_a_Page_111thm.jpg
231498e9e44fba46e34e895a1444702d
176901b7244ae0ae968752c7cd838a32dfc8a258
6001 F20101113_AAAPFY hirsh_a_Page_092thm.jpg
9a7acd456fda5d24e95d8ec3358c6719
7236f96c971dd6f6dc139f86cd3db6de28718d52
50553 F20101113_AAAODK hirsh_a_Page_059.pro
3e8cc0006d7d83988dda5922b6b65e50
97a429317398e82d06a4e8d563f4d16e5b028021
6995 F20101113_AAAOCW hirsh_a_Page_040thm.jpg
3ee00a262ee4fe85b8ca3cb9c28f2d92
17930e785303177ac4f78daaa804867e42ba8563
36542 F20101113_AAAPGN hirsh_a_Page_111.QC.jpg
ab4cd985ca5ddc2790195a2b5c927c68
b79ac30101089d08e2cb7677e3cac0b79ee343ef
2358 F20101113_AAAPFZ hirsh_a_Page_093thm.jpg
48c90f6fbb96203b60cc7c47e8d3cd82
fe2a4fb2b02290fafa6a1f8d9877421248857f2b
33959 F20101113_AAAODL hirsh_a_Page_087.jp2
6a61267cc318df9215b506bfe20ee54b
c466ef6b3184fa52e6653ef060c73474fb9b5e21
113845 F20101113_AAAOCX hirsh_a_Page_071.jpg
6dcd745cac8adc7cdd3e9f4a09c4998b
7bffbbfcd544f49ad97b1e8d4ddf11f7a7e52d8a
10666 F20101113_AAAPGO hirsh_a_Page_115.QC.jpg
ec3d2a9f5b49846602ef22b2cf97bde8
f6f7913622a248fc57dcf3e558061006d73f69e6
8676 F20101113_AAAODM hirsh_a_Page_050thm.jpg
4e9c31cecd232801d8c2fed3404fa6f2
e8aaddb24421b734c79a4cab4e4583b5e9a5686c
F20101113_AAAOCY hirsh_a_Page_106.tif
c232b41d7929cad0e4d8b3a42a4080cf
fdb3697a9299fbe2332fa8ccf66e5a534d7d19b3
36613 F20101113_AAAOEA hirsh_a_Page_104.QC.jpg
3c3f745a1a30935208a7adeab7d49df8
03b1e57267c18dbd61acfd4af6cbdcb7b52a190d
F20101113_AAAODN hirsh_a_Page_043.tif
046f3fd06d26d0c802def03f2ab1c30d
9f14aa3bb98ed85d0176bcc8a1b489a7fd3ef8ee
F20101113_AAAOCZ hirsh_a_Page_101.tif
71a59e8a2cd1d79597b486358df3d4dd
6233e26d825b939aa824fc21b92b0d4dd7320c94
2639 F20101113_AAAOEB hirsh_a_Page_105.txt
fb8faec44861812dfea88b020c63f4c2
cc2c6a65d03350cfe1dd951217ea9383b7068993
2049 F20101113_AAAODO hirsh_a_Page_049.txt
d90a4fd43726b858ecc27ff77273f859
7dcbdf82ebf6e5298c5b09c5a7c1f7b57c1c402b
F20101113_AAAOEC hirsh_a_Page_074.tif
0192f2de492e9297f4a5f32f847de669
6683668a553c0f9fcf7069ea8863968e97ef1b8e
24101 F20101113_AAAODP hirsh_a_Page_032.pro
306855416be7444670ffcae0f054d00a
5ac444c72a6695006b10a1a27ab55e5f73f8fc08
77429 F20101113_AAAOED hirsh_a_Page_094.jpg
81c4d2afa84225284e695233e9184871
58495bc21d58f50d204640197ab2f92401b00be3
23314 F20101113_AAAODQ hirsh_a_Page_098.QC.jpg
e1d5fab5dd2188b3016f9493f076437e
1662266091ea79462a9fd90464fe2568c96fc4ca
2563 F20101113_AAAOEE hirsh_a_Page_115thm.jpg
7249284f3065af2e6d6881440a4193fa
16d8971057f687ee8fc6b058bc2499bbc84e9782
5562 F20101113_AAAODR hirsh_a_Page_094thm.jpg
41eac8a5f72cba6c03df0e4fc8acab25
f5ad98356f76de07bde7cfd5bf8cf633a18d6acf
114936 F20101113_AAAOEF hirsh_a_Page_050.jp2
84f2d44c4b1331a833750306f56d5d79
0fa678ad45d1e2975b7fda0be22bfe48a6eeb85d
14179 F20101113_AAAODS hirsh_a_Page_063.pro
de9a12673ddb435a49d1893da932d180
26b105659caf354440e908fefce843c0a15a99f8
110850 F20101113_AAAOEG hirsh_a_Page_038.jpg
ed29ff704782fbe23e2a7b8a04241f0e
13ddf4896114e94f7a9ed6eb83fb800048fe04b8
35377 F20101113_AAAODT hirsh_a_Page_066.QC.jpg
7718d7ed4a0e3cc479b47984c1440260
31bb1c6e93546bf3248643e1bede37ddf66d32b1
F20101113_AAAOEH hirsh_a_Page_006.tif
f3587e11c243a442197d54fae0d6d9c8
bac5fc29dd1b8560b4e72a401f2a528c14da6b8d
80708 F20101113_AAAODU hirsh_a_Page_090.jp2
61cd5bb3fc1f365e9447e5beacc42cf9
20c66572c9782dfaf511145024024bc5fcb3f1c6
F20101113_AAAOEI hirsh_a_Page_023.tif
91a4b1b5d22ed8b59b063357b9d61f9f
c3887e449d09987331dd16230d17bec818dcc515
F20101113_AAAODV hirsh_a_Page_017.txt
b17834d34973a6caafc9f7837238e648
4d1012c4fadb6c3a8de6ccd27f0f5b6376cc3563
115495 F20101113_AAAOEJ hirsh_a_Page_010.jpg
5a11447c0d9f1ee87b26ac25eb654927
da9583dc9d4ef164e64af7c3cc43037a690a292b
2242 F20101113_AAAODW hirsh_a_Page_065.txt
040029f759106f4883b0ed41b9a5c68a
dd6d2ab77b32d91637c66517166720a7b512efeb
9036 F20101113_AAAOEK hirsh_a_Page_109thm.jpg
28c2d919944502fb7b5c6df73620bd27
2265e33c296da148088866017b2c6f3c60aed328
99416 F20101113_AAAODX hirsh_a_Page_042.jpg
bd764d4f3d96ef4028043692e1761f23
c9b709c2fa4aa79f556633bdb34b74d58b376306
114094 F20101113_AAAOEL hirsh_a_Page_022.jpg
837ec6715c64c9a73a4bf42beda744bc
b25a3cd66709403082903388b96220ccd0f48df3
2014 F20101113_AAAODY hirsh_a_Page_059.txt
859bfebc52048176ec204a3725d62eb2
445796707c89954a009a81a3dbe4187f0834e775
37156 F20101113_AAAOFA hirsh_a_Page_072.QC.jpg
106b9aebe2b07fa66e89322912a13fa3
d9e863c6b30145aef5e1e1cd5a992d755d945e0f
F20101113_AAAOEM hirsh_a_Page_105.tif
5a4954319700f3c9b978db2087a8d3bf
7bc2bd8f69a61ecbba53db3b894478e04a35c43e
55236 F20101113_AAAODZ hirsh_a_Page_076.pro
593aee485b0ffcf3153d80deba91b643
009c1c01f7af15ee3298e2ad538270cbd69eb903
55559 F20101113_AAAOFB hirsh_a_Page_081.pro
8a2d2d1c51a3c51030f29cda98b34813
bf9e9793a87a9187d099aa94418f1a93d747e47a
F20101113_AAAOEN hirsh_a_Page_068.tif
66266326376ae381d45c174b95ff9c9b
f61c7e9d6d346053f2ccd4a87aa1e3f845862ab4
F20101113_AAAOFC hirsh_a_Page_036.tif
945c5e01a95c65b7d7b0ebbce8a136be
9647ac6c0f128368b563ccdfd37a6382100dc8ac
9028 F20101113_AAAOEO hirsh_a_Page_105thm.jpg
192947355995dbce64eb152b274e849a
b2823e4f2a706558f094faa903c91cc6a7190ec1
76923 F20101113_AAAOFD hirsh_a_Page_100.jpg
9bdec509030287cc36dd70be0eb95784
244554f5d07f4be95d3f055792d327b8929b2b5d
36136 F20101113_AAAOEP hirsh_a_Page_052.jpg
7b0aa3bb7706a1805d8ee7ffd13cef66
11e8f5477ce40d70f3bef1e5e82ef726670c253c
9232 F20101113_AAAOFE hirsh_a_Page_072thm.jpg
29bc4929621438b596944ada86cd6cd6
7515d568fbb41aa2cb356e85a2b27731955c8822
34922 F20101113_AAAOEQ hirsh_a_Page_076.QC.jpg
23bdf7c7d543d684e9e6670952cc03f2
9b05ffea989f6a05e810ed03c67dde50f414d36b
F20101113_AAAOFF hirsh_a_Page_071.tif
7d84408d5935914f3e31c649e7fb17b8
0a004f711cb7c8404d0cae9708452e5b7bc8c2f6
F20101113_AAAOER hirsh_a_Page_020.txt
00658566e0cbbbe44ff8ca5ab422ea7a
0e5685626c8bd52d458d12ef3a83a41b3976ccf4
F20101113_AAAOFG hirsh_a_Page_080.tif
1f962ba357e4fa541c486df326d59d2e
96e0d1b07e0151c580b9e951d9a1d2f4b2ec113a
125715 F20101113_AAAOES hirsh_a_Page_111.jpg
a2b92800d15e112866e9c67df6e3ddf1
02a7b4fbe718b1b1c31e7c5034f6c34d586435cf
113379 F20101113_AAAOFH hirsh_a_Page_067.jp2
7b137ce032eb4683c1d74921d5ff67b4
c2c975b70c755a56974c4198a520a2ee99fc88ea
24430 F20101113_AAAOET hirsh_a_Page_094.pro
51f1d5341d497f893ab3c084499c5466
e9cec37d143184a1e3f6503115af6db6a1ad2820
44052 F20101113_AAAOFI hirsh_a_Page_064.pro
eda241a9caef264e4d94ddff72a661a9
2ff1ea8cf8db3d4a88743b313325a816cb05e1a6
3788 F20101113_AAAOEU hirsh_a_Page_002.jpg
46049d67a6d399c1fa16dd0e3c49d164
e87cd444fb0fd098970e223cb07ce9888337b16b
8846 F20101113_AAAOFJ hirsh_a_Page_038thm.jpg
2300ee1ce775f663fb1b26494459f8e9
9922df391dfb55415eed215e890c9ba463fd0552
113970 F20101113_AAAOEV hirsh_a_Page_004.jpg
a5acff043919d4a402eb18cf4b81ac37
e1b8a9a807def0ce401dd0a915a3b17a3135f4bf
F20101113_AAAOFK hirsh_a_Page_050.tif
d6a9f1bc1a4b37e5a048d8a0a52b6157
5945d73d38bab410d5a707a8c18db09fc3729ae7
55914 F20101113_AAAOEW hirsh_a_Page_051.pro
ef8e7e6ddd4e4693ed86706c9e3a77c9
724fbb7f3e157364c1065e24bfde605ba3190353
23892 F20101113_AAAOFL hirsh_a_Page_092.QC.jpg
079cd770b143b59debd1a3c844b4b651
b64434c6c4ae246e9e3f89a280c58e5e2a4ac481
F20101113_AAAOEX hirsh_a_Page_093.tif
2206f7eb82c6fef0ca29af6b4b65c891
7dee6c9887738d5ec704810bc22fbbf046aad653
124300 F20101113_AAAOGA hirsh_a_Page_104.jpg
077b96ef77dd4cda8f569cdd7738a5d2
b0b67becd02df36259dc5c0f8fade9b7b7837cc2
2567 F20101113_AAAOFM hirsh_a_Page_104.txt
5206724315ba54edacb2217451c68686
3aea07e2419f53049f738f8f9eeccfbe994b14cd
9233 F20101113_AAAOEY hirsh_a_Page_112thm.jpg
e4dc00756922f5352b61d2151c561290
0590f70a83051084adb13301603f07b47149a0bb
F20101113_AAAOGB hirsh_a_Page_100.tif
5de274465d014d033e2f0f42ebe17006
9708e00736f099122ad8f4c0b0b1f77e6f7304fd
91372 F20101113_AAAOFN hirsh_a_Page_085.jpg
54ae9027ead39cb8a17028ebb6f3dc50
80b2bcdec9f08c46ac83f716a487f1bb876f3361
9284 F20101113_AAAOEZ hirsh_a_Page_069thm.jpg
2756962011efab8d420504a591a6eef0
5c7b066cdd612b502106ab2ffedeb57a4edb3b8d
F20101113_AAAOFO hirsh_a_Page_059.tif
d3dfcfa790837511975746700b5ae423
8c4eb15c934a29b21bc8823fa8d84bf4a0cb27c8
110668 F20101113_AAAOGC hirsh_a_Page_014.jpg
0dea21b4c3957310a07c3a7066c56c03
58879f769ad57e0c726beeb238aeba007caa4acf
F20101113_AAAOFP hirsh_a_Page_014.tif
37caa183569daf4c3b5623f38486a30f
bec4cc92f23982b2e9e7626dc5be94f35baba816
32534 F20101113_AAAOGD hirsh_a_Page_114.pro
dd75f99461ed5fa9e2f807878d0758be
86e7090b3d8fd88fed0607138511679eb5302921
2182 F20101113_AAAOFQ hirsh_a_Page_079.txt
d2667b87042f07915c6b42ec5bbf52c6
b073baa1e0f230eab7ca9dabaa68393f35de8f72
5007 F20101113_AAAOGE hirsh_a_Page_114thm.jpg
a38f6547dbcc0785fe9adecb7375aa42
b5f829854e4addac1559bd6670affc1ae3751e11
112505 F20101113_AAAOFR hirsh_a_Page_061.jpg
1da8954c503feacdeb774a7dbf02bea3
c1cfb93a4b739fdf5aa8e9cf764b5e17238bc686
2139 F20101113_AAAOGF hirsh_a_Page_028.txt
4e231c682205eda19f9bf6918365138e
ce77cf5a3235ccd1b31bf3b488bfff93406c5708
56013 F20101113_AAAOFS hirsh_a_Page_080.pro
89d55bb9a68cae72e45cfa7402250871
cb8704ac94fb8ea2316b1777e3a1bb52fddde310
2173 F20101113_AAAOGG hirsh_a_Page_011.txt
ff606a4dcd1d93d8ea84e14483fdaee1
b9faa3d02066d87c57eef0c94c545ded2688059a
F20101113_AAAOFT hirsh_a_Page_075thm.jpg
30cf317aafd4bd4d8212ac2c5eeb997c
418e496c9585395350339ab6561efff194bf7719
93403 F20101113_AAAOGH hirsh_a_Page_044.jpg
9faa3d78f8afe7bc63d53eda87b4f4b5
c589628be20b181bae0fe740adb31a0ba4aba685







INVESTIGATING PATIENT AND PROVIDER INFLUENCES ON THE ASSESSMENT
AND TREATMENT OF PAIN:. A NOVEL VIRTUAL PATIENT TECHNOLOGY
APPLICATION





















By

ADAM T. HIRSH


A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2008






































O 2008 Adam T. Hirsh









ACKNOWLEDGMENTS

I thank Michael Robinson for his exceptional mentorship throughout my graduate

education. He has provided the perfect combination of freedom and structure for my

development as a scientist. I also thank my dissertation committee Roger Fillingim, Steven

George, and William Perlstein for their time and energy. They have each served as models of

academic excellence. Special thanks are extended to Shankar Manamalkay for his expert

technical assistance, and to my colleagues in the Center for Pain Research and Behavioral Health

for their friendship and support. I thank the NIH and my Program Director, Linda Porter, for

funding this work and my graduate education. Thanks are also extended to the University of

Florida and Department of Clinical and Health Psychology for providing a first-rate training

environment. I thank my family for their support throughout this and previous endeavors.

Finally, I thank my wife Sarah, without whom none of this would be possible.












TABLE OF CONTENTS


page


ACKNOWLEDGMENTS .............. ...............3.....


LIST OF TABLES ........._... ......___ ...............6....


AB S TRAC T ......_ ................. ............_........7


CHAPTER


1 INTTRODUCTION ................. ...............9.......... ......


Pain Assessment .............. ...............10....
Influence of Sex ................. ...............11........... ....

Influence of Age .................. ...............13................
Influence of Race/Ethnicity ................. ...............15................
Pain Treatment............... ...............1
Influence of Sex ................. ...............18........... ....

Influence of Age .................. ...............20........... ....
Influence of Race/Ethnicity .................. ....... ....... ...............22......
Limitations of the Assessment and Treatment Literature ................. ......... ................25
Facial Expression of Pain ................ .......................... .................. ......26
Lens M odel Design ............. ...... ._ ...............30....
Rationale ............. ...... ...............31....


2 M ETHODS .............. ...............33....


Participants .............. ...............33....
M measures ............... ... .... ..... .............3

Demographics Questionnaire .............. ...............34....
Gender Role Expectations of Pain ............. .....___ ...............34..
Lens M odel Design ............. ...... ._ ...............35....
Profiles............... ...............36

Judgm ents ............. ...... __ ...............37....
Procedure .............. ...............3 8....

Hypotheses................ ..............3
Pain Assessment ............... .........__ ......_ .............3
Treatment with Non-opioid Medication ...._ ......_____ .......___ ............4
Treatment with Opioid Medication .............. ...............40....
Recommendations for Change in Medications............... ..............4
Healthcare Provider Characteristics .............. ...............42....
Statistical Analyses ............. ...... ._ ...............42....

Idi ographi c............. ...... ._ ...............42....
Nom othetic .............. ...............42....













3 RE SULT S .............. ...............44....


Participants .............. ...............44....
Pain Assessment Policies............... ...............44
Pain Intensity .............. ...............45....
Pain Unpleasantness .............. ...............46....
Mood Assessment Policies .............. ...............47....
Positive Mood ................. ...............47.................

Negative M ood .............. ...............47....
Treatment Decision Policies ................. ...............48........... ....
Non-opioid Medication .............. ...............48....
Opioid Medication............... ...............4
Recommendation Policies ............... ...............49....

Change in Non-opioid Medication ................ ...............49................
Change in Opioid Medication .............. ...............50....
Number of Significant Cues .............. ...............51....
Significance of Contextual Cues .............. ...............5.....1
Number and Significance of Overall Policies .............. ...............53....
Within-cue Comparisons .............. ...............54....
Pain Assessment ................. ...............54.......... .....
Mood Assessment............... ...............5
Treatment Decisions ................. ...............57.................
Recommendations .............. ...............58....
Self-reported Cue Utilization.................. ........ ...........5
Knowledge of Study Hypotheses and Cue Utilization .............. ...............59....
Exploratory Group Analyses .............. .... ... ....................6
Participant Characteristics and Overall Decision Policies .............. .....................6
Participant education and overall decision policies ............... ...................6
Participant professional experience and overall decision policies ...........................61
Participant Characteristics and Cue Utilization............... ..............6
Participant sex and cue utilization............... ..............6
Participant education and cue utilization............... .. .............6
Participant professional experience and cue utilization .............. ....................6


4 DI SCUS SSION ................. ...............65................


APPENDIX RESULTS OF IDIOGRAPHIC REGRESSION ANALYSES..............._._._.........86


LIST OF REFERENCES ....._.__................. ...............102 .....


BIOGRAPHICAL SKETCH ................. ...............115......... ......










LIST OF TABLES


Table page

3-1 Demographic and background characteristics of participants............_ .........___.......45

3-2 Number of significant cues at each policy ......___ ........._._ ...._. ..........5

3-3 Variance in decision policies explained by contextual cues ........._._ ...... .._..._.........53

3-4 Descriptive data on overall policy capturing .............. ...............54....

3-5 Means and standard deviations for ratings within cue .....__.___ ........._. ................55

3-6 Number of participants with significant overall policies .........._..._........_ ...............60

3-7 Participant use of demographic and pain expression cues ....._____ .... ... ..___............63

A-1 Policies toward pain intensity assessment .............. ...............86....

A-2 Policies toward pain unpleasantness assessment ................. ...............88...............

A-3 Policies toward positive mood assessment .............. ...............90....

A-4 Policies toward negative mood assessment .............. ...............92....

A-5 Policies toward non-opioid treatment ................. ...............94........... ...

A-6 Policies toward opioid treatment .............. ...............96....

A-7 Policies toward change in non-opioid treatment............... ...............9

A-8 Policies toward change in opioid treatment ................. ...............100........... ..









Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy

INVESTIGATING PATIENT AND PROVIDER INFLUENCES ON THE ASSESSMENT
AND TREATMENT OF PAIN:. A NOVEL VIRTUAL PATIENT TECHNOLOGY
APPLICATION

By

Adam T. Hirsh

August 2008

Chair: Michael E. Robinson
Major: Psychology

Pain is a misunderstood and mistreated symptom of acute and chronic illness. Patient

demographic characteristics and nonverbal communication displays have been found to influence

the assessment and treatment of pain. Numerous methodological limitations of these previous

investigations constrain the research questions that could be addressed and the conclusions that

have been yielded. The current analogue study employed an innovative research design and

novel virtual patient technology to investigate clinical decision making for pain assessment and

treatment. Fifty-four currently practicing nurses participated in this study delivered via the

Internet. Thirty-two vignettes of virtual patients were presented; each vignette contained a video

clip of the patient and clinical summary information describing a post-surgical context. Nurses

were asked to make decisions in the following domains: 1) pain intensity and unpleasantness

assessment; 2) positive and negative mood assessment; 3) non-opioid and opioid medication

treatment; and 4) recommendation for a change in non-opioid and opioid medication. The patient

demographic cues of sex, race, and age, as well as facial expression of pain, were systematically

manipulated across vignettes and hypothesized to influence assessment and treatment ratings.

Idiographic and nomothetic statistical analyses were conducted to test these hypotheses. Results










indicated that at the idiographic level, patient demographic and pain expression cues accounted

for significant, unique variance in assessment and treatment policies among many nurse

participants. In several instances, the direction of the demographic cue effects was unexpected

and counter to a priori hypotheses. Patient pain expression was the most prominent cue

throughout these policy domains. Within-cue differences emerged in the aggregate; the size and

consistency of these differences varied across policy domains. Exploratory analyses were

suggestive of the role of provider education, professional experience, and practice setting on

pain-related decisions. The current investigation demonstrates the application of novel virtual

patient technology to the study of pain-related decision-making. These data indicate that patient

demographic characteristics and facial expressions of pain often play a significant role in the

assessment and treatment of acute post-surgical pain. Implications of the present findings are

discussed in the context of the extant literature. Methodological considerations and future

research directions are also discussed.









CHAPTER 1
INTTRODUCTION

Despite recent increases of pain management content in the literature (Ferrell et al., 1993),

the development of specific pain curricula for several pain-related health disciplines

(International Association for the Study of Pain [IASP], 1993, 1997), and the availability of

clinical practice guidelines (World Health Organization [WHO], 1986; American Pain Society

[APS], 1992; Acute Pain Management Guideline Panel, 1992), pain remains a misunderstood

and mistreated symptom of acute and chronic illness. Previous research has estimated that more

than 80% of pain sufferers receive insufficient pain relief, largely due to excessively

conservative pharmacologic treatment (WHO, 1986). Because pharmacotherapy with analgesic

medications is one of the primary foundations of pain management, overly conservative

approaches may deny adequate pain relief to increasing numbers of patients.

Poor pain management due to insufficient administration of analgesic medications is likely

the result of several interacting factors (Portenoy, 1996). Many of these factors are the product of

inadequate knowledge and inappropriate attitudes on the part of health care providers about pain

in general, and pain assessment and treatment in particular. In compensating for knowledge

deficits concerning pain assessment and pharmacologic pain management, providers may

wittingly or unwittingly permit their own biases to exert undue influence over clinical decision-

making. Consequently, less knowledgeable providers may mismanage pain and, thus, needlessly

prolong the suffering of patients through the implementation of unsystematic clinical decision

policies. Thorough investigations of those factors that influence the clinical decisions of

providers regarding pain assessment and the administration of analgesic medications is,

therefore, necessary to improve the care of patients in pain.









Pain Assessment

An individual's assessment of the pain experience of another person is likely based on

many factors. In the clinical context, medical and disease related variables of the observed

patient are of clear importance. Additionally, characteristics of the observer, such as his/her

beliefs regarding appropriate pain behaviors and stereotypical social and gender roles, as well as

his/her acuity in observing overt behaviors, are hypothesized to have a large impact on the

assessment of pain in others. In addition, qualities of the person who is being observed, such as

sex, age, and ethnicity, must also be considered (Teske, Daut, & Cleeland, 1983). The observer's

perception of the pain experience of another, then, most likely results from an interaction

between characteristics of the observer and the person that individual is observing.

In the clinical literature, there has been a considerable amount of attention paid to nurses'

estimation of patients' pain. Much of this literature has focused on pain assessment accuracy,

with mixed results. A frequently cited study by Zalon (1993) found that nurses' visual analogue

scale (VAS) pain ratings were significantly, yet modestly, correlated with the pain ratings of

postoperative abdominal surgery patients. Interestingly, these nurses over-estimated mild pain

and under-estimated more severe pain. Patients' pain was the only significant factor accounting

for approximately 9% of the variance in nurses' assessments that was related to the accuracy of

nurses' pain assessments. These findings parallel those of other studies, revealing small but

significant correlations between nurses' pain assessments and patients' self reports, and the

tendency for nurses' to misestimate patients' pain (Choiniere, Melzack, Girard, Rondequ, &

Paquin, 1990). Similarly, Salmon and Manyande (1996) found that nurses frequently

underestimate patients' need for analgesia and their ability to cope with pain. Others, however,

have found little to no agreement between the pain ratings of nurses and patients, presumably

because these nurses relied exclusively on observed behavior (Thomas, Robinson, Champion,









McKell, & Pell, 1998). This body of literature suggests that health care providers often make

inaccurate judgments regarding the level of patients' pain. The concerning implication is that

these misestimates influence the decisions that providers make regarding medication

administration for pain.

Influence of Sex

As noted above, sex of both the observer and observed, in addition to the observer' s beliefs

about sex and gender, may influence the pain assessment process. There has been a recent

increased interest in sex and gender differences in pain. Research has shown that there is a

discrepancy between the relatively small sex differences in clinical pain report and the moderate-

to-large differences in experimental pain report. Robinson and colleagues developed the Gender

Role Expectations of Pain (GREP) questionnaire to explore the hypothesis that the differences in

experimental pain are an artifact of the laboratory setting where gender roles are activated. Their

research has shown that males and females report significant differences in their pain

expectations for self and others (Robinson et al., 2001). Importantly, these expectations are

associated with experimental pain responding, and have been shown to explain more variance in

pain reports than sex (Wise, Price, Myers, Heft, & Robinson, 2002). Subsequent research

focused on the assessment of pain in others. Specifically, this research was conducted to

determine if males and females perceive pain and pain-related emotions in others differently

based on the sex of the individual (Robinson & Wise, 2003). This study involved participants

viewing videotaped recordings of others undergoing a cold pressor task, after which they

provided ratings of perceived pain experience of the videotaped participant. Results indicated

that (1) viewers rated male videos as having less pain than female videos; (2) for both male and

female videos, female viewers rated observed pain intensity higher than did male viewers; (3)

both male and female video participants' pain was underestimated, but males' pain was









underestimated more than females' pain; (4) expectations of gender-related endurance of pain

significantly predicted rating of both male and female videos; (5) when endurance expectations

were controlled, sex of the viewer no longer significantly predicted observed pain ratings.

The data from these studies suggest that not only do gender stereotypes influence one's

own pain ratings and pain behavior, but they also influence one' s perceptions of the pain

experience of others. This research was conducted in the laboratory and investigated

experimental pain. As such, these findings cannot directly address the clinically relevant issues

of whether gender role expectations influence pain assessment and decisions about treatment and

prescription practices for pain in the medical setting. These Eindings do, however, underscore the

need for such research, which has heretofore not been conducted. The few investigations that

have been conducted on the influence of patient sex on clinical pain assessments are mixed. In an

early study of cancer patients, a greater patient-provider discrepancy about the perceived severity

of the patient' s pain was found for females (Cleeland et al., 1994). A later study of 281 minority

cancer patients found no sex differences in the proportion of males and females whose pain was

underestimated by their physicians (Cleeland, Gonin, Baez, Loehrer, & Pandya, 1997). In this

study, underestimation was high for both male and female patients (66% and 72%, respectively).

In a more recent study of minority cancer patients, Anderson and colleagues (2000) did Eind

evidence of a sex disparity in pain assessment. Consistent with results from Cleeland et al.

(1994), they found that physicians underestimated the pain severity of 79% of female patients

compared with 59% of male patients.

The important contribution of these investigations to the pain assessment literature

notwithstanding, the generalizability of their Eindings may be constrained for several reasons.

First, all were investigations of cancer patients. The implications and appraisals of cancer-related










pain may be very different from non-malignant pain conditions, given that cancer is a life-

threatening illness. This may impact on both the patient and provider assessment of pain.

Second, the racial/ethnic status of the patients in these studies may have confounded the results.

Both the Cleeland et al. (1997) and Anderson et al. (2000) studies included only patients of

racial/ethnic minority status; the Cleeland et al. (1994) study did not report the racial/ethnic

characteristics of their sample. Given that minority individuals are at increased risk of having

their pain under-assessed and under-treated (see below), this fact may complicate interpretations

of these investigations. For example, the null finding of the Cleeland et al. (1997) study may be

due to the overwhelming ubiquity of pain underestimation in these minority patients, which may

have obscured any sex difference in pain assessment. Finally, the vast maj ority of providers in

these studies were Caucasian male physicians. Since patient-provider demographic congruence

may influence the medical encounter (Anderson et al., 2003), and the number of providers from

diverse racial/ethnic and sex backgrounds is increasing (American Medical Association [AMA],

2005; Girard, 2003), future investigations that include a diverse range of providers are certainly

warranted.

Influence of Age

The experience of pain is common in older populations, with prevalence estimates ranging

from 45-80% depending on the residential status of the sample (Fries, Simon, Morris, Flodstrom,

& Bookstein, 2001; Herr, 2002; Mobily, Herr, Clark, & Wallace, 1994; Teno, Weitzen, Wetle, &

Mor, 2001; Weiner, Peterson, & Keefe, 1999; Werner, Cohen-Mansfield, Watson, & Pasis,

1998). Despite these estimates, pain assessment in the elderly is poor relative to younger

populations (Ferrell, 1996; Gloth, 2000; Horgas & Elliott, 2004). The assessment of pain in older

populations is complicated by several factors. Older adults may hold the belief that pain is a

normal and expected part of aging and, thus, fail to adequately communicate their pain










experience to others (Ferrell, 1995). Indeed, older adults tend to under-report pain relative to

younger populations (Bellville, Forrest, Miller, & Brown, 1971; Oberle, Paul, Wry, & Grace,

1990). Since self-report is the gold standard of clinical pain assessment, this tendency increases

the likelihood of a sub-optimal outcome of the pain assessment process in the elderly patient.

The self-report of pain is further complicated in older adults by the paucity of adequately

standardized assessment instruments for this population. The psychometric properties of

instruments that have been thoroughly standardized in younger patients [e.g., VASs, McGill Pain

Questionnaire (MPQ; Melzack, 1975)] are often compromised when employed in the elderly

(Gagliese, 2001).

Higher rates of medical comorbidities in the elderly may also impact on the assessment of

pain in these patients, as such comorbidities may compete for the attention of healthcare

providers (Nishikawa & Ferrell, 1993). Among the possible comorbidities, dementia is of

particular importance. Not only does dementia have the likelihood of competing for the attention

of providers, but, perhaps more importantly, it introduces a level of complexity to the pain

assessment process that is unmatched by other conditions. As the severity of dementia increases,

the level of self-awareness and ability to communicate decreases (White & Cummings, 1997).

The pain assessment process is consequently compromised. Evidence for this is found in the

substantial literature documenting the substandard assessment of pain in elderly patients with

cognitive dysfunction (Cohen-Mansfield & Lipson, 2002a, 2002b; Cook, Niven, & Downs,

1999; Kaasalainen et al., 1998; Ferrell, 1995; Sengstaken & King, 1993).

A final complication of the assessment of pain in older adults concerns the attitudes and

beliefs of providers. Research suggests that providers may be overly cautious in considering the

pharmacologic management of pain in general (Portenoy, 1996), and this approach may be










heightened in the elderly (Aubrun, 2005). This cautiousness is likely to impact the pain

assessment process, since this process is undertaken for the primary purpose of informing

treatment decisions. Providers may also hold beliefs similar to the elderly themselves concerning

the normative experience of pain in this population and/or the decreased pain sensitivity of older

adults (Sarkisian, Hays, Berry, & Mangione, 2001). These beliefs may, in turn, inappropriately

influence the pain assessment of such patients.

It is unlikely that the factors noted above operate in isolation. Rather, they are likely to

interact with each other and other variables to influence the assessment of pain in elderly

patients. Although the extent of this interaction has not been entirely elucidated, it is clear that

elderly individuals are at increased risk of sub-optimal pain assessment and, consequently, pain

management. As such, continued work in this area appears warranted.

Influence of Race/Ethnicity

There has been a recent surge of investigations on race/ethnic differences in pain

experience. In the experimental context, African-Americans and Hispanics reliably demonstrate

lower pain tolerance and higher pain unpleasantness than Whites across a range of pain stimuli

(Campbell, Edwards, & Fillingim, 2005; Edwards & Fillingim, 1999; Walsh, Schoenfeld,

Ramamurth, & Hoffman, 1989). Race/ethnic differences in pain perception have also been

reported for several chronic pain conditions, including AIDS (Breitbart et al., 1996); glaucoma

(Sherwood et al., 1998); migraine (Stewart, Lipton, & Liberman, 1996); arthritis (Creamer,

Lethbridge-Cejku, & Hochberg, 1999); postoperative pain (Faucett, Gordon, & Levine, 1994);

and myofascial pain (Nelson, Novy, Averill, & Berry, 1996). There is also an expanding

literature concerning race/ethnic disparities in the treatment of pain, which is reviewed below.

Differences in the pain assessment process have been investigated as one possible explanation

for these treatment disparities. The results of these studies are mixed. On the one hand,









race/ethnic differences have been reported in the ability of health care providers to accurately

interpret patient pain. For example, results of a large multi-center study indicated that minority

cancer patients were more likely to have the severity of their pain underestimated by their

physicians than White patients (Cleeland et al., 1997). Sheiner and colleagues (1999) found that

although Jewish and Bedouin parturients gave similar self-reports of pain, the medical staff -

consisting entirely of Jewish providers perceived Bedouin women as experiencing less pain

than Jewish women. Although their study included only minority patients with cancer and, thus,

did not allow for comparisons to Caucasian patients, Anderson and colleagues (2000) found that

physicians underestimated the pain severity of 74% and 64% of African-American and Hispanic

patients, respectively. In addition to being at increased risk of having their pain misestimated,

there is evidence that minority patients may also be less likely to have their pain documented

altogether (Bernabei et al., 1998).

A number of possible explanations for these disparities exist. Race/ethnic differences in

language and communication, socioeconomic status, access to healthcare, symptom

manifestation, and patient preference may all play a role in this context. Provider bias must also

be considered. It is important to note, however, that such disparities in pain assessment are not

always found. For example, Todd, Lee, and Hoffman (1994) investigated the concordance

between patient and provider pain assessments in the Emergency Department. Caucasian and

Hispanic patients presenting with isolated extremity fracture were included in this study.

Although physician estimates of pain were lower than patient reports, there was no difference in

physician estimates of pain between the groups. Furthermore, in contrast to the finding noted

above that minority patients may be less likely to have their pain recorded (Bernabei et al.,

1998), Todd, Deaton, D'Adamo, and Goe (2000) found no difference in the frequency of pain









documentation between Caucasian and African-American patients presenting to the Emergency

Department.

An important ambiguity of many of these studies is whether the difference if one is

found in provider estimation of patient pain is the result of factors within the medical staff who

rated the pain (e.g., knowledge deficits, bias), or factors related to the patient groups (e.g.,

communication, SES). Future investigations are needed to elucidate these issues. Such

investigations have the potential of improving the pain assessment process and, consequently,

treatment of all patients in pain.

Pain Treatment

Optimal treatment of pain is an important goal of healthcare. Complications of unrelieved

pain are widespread and varied. Physical, functional, and psychologic conditions are associated

with and exacerbated by pain. These include depression, sleep disturbance, and functional

impairment (Ferrell, 1995; Herr & Mobily, 1991; Lavsky-Shulan et al., 1985; Parmalee, Katz, &

Lawton, 1991; Williamson & Schulz, 1992). There is also evidence of increased morbidity and

mortality secondary to poor pain control (Cousins, 1991). Despite recent increases of pain

management content in the literature (Ferrell, McGuire, & Donavan, 1993), the development of

specific pain curricula for several pain-related health disciplines (IASP, 1993, 1997), and the

availability of clinical practice guidelines (WHO, 1986; APS, 1992; Acute Pain Management

Guideline Panel, 1992), pain remains a misunderstood and mistreated symptom of acute and

chronic illness. Approximately 23 million Americans experience post-operative pain each year,

and as many as 75% do not achieve adequate pain control despite the availability of effective

treatments (Cousins, 1994). In terms of chronic pain, the difficulties of managing this condition

are axiomatic. Of the many possible contributing factors that may account for the overwhelming









evidence of poor pain management practice, patient demographic characteristics have been the

target of recent empirical focus.

Influence of Sex

The accumulation of evidence from both the laboratory (e.g., the influence of gender role

stereotypes on pain assessments) and the clinical setting (e.g., the inaccuracy of health care

providers' pain assessments of patients) suggests that an individual's sex may influence the

assessment of pain in others and, thus, may elicit differential treatment practices from health care

providers. Research investigating clinicians' beliefs regarding sex differences in pain perception

is equivocal. Some studies indicate that many health care providers believe differences in pain

perception between males and females do exist (McCaffrey & Ferrell, 1992), while others reveal

no such belief among providers (Holm, Cohen, Dudas, Medema, & Allen, 1989).

In terms of actual pain management practice, the literature is similarly conflictual. Results

of many investigations indicate that females are at increased risk of having their pain under-

treated relative to males. Retrospective studies have demonstrated a tendency for males to

receive a higher frequency of narcotic analgesics (Calderone, 1990) and larger initial doses of

pain medication (McDonald, 1994) post surgery. This sex disparity in post-operative pain

management has also been found in pediatric and elderly samples. In one study, men and boys

were prescribed significantly more narcotic medication following cardiothoracic surgery than

similar females (Beyer, DeGood, Ashley, & Russell, 1983). Faherty and Grier (1984) reported

that medical providers prescribed significantly less pain medication for adult females of all age

groups, including the elderly, following abdominal surgery compared to males. Female cancer

patients with pain have also been found to be under-medicated relative to males (Cleeland et al.,

1994). In more experimental designs, research employing identical vignettes, save the sex of the









patient, has shown that nurses choose less analgesic medications to be administered to females

than males (Cohen, 1980; McDonald & Bridge, 1991).

Evidence that patient sex does not influence pain management practices has also been

reported. Bartfield and colleagues (1997) conducted a prospective study on adult patients with

acute low back pain presenting to the Emergency Department. Patient sex did not emerge as a

significant predictor of analgesic administration in this sample. Similar findings were reported by

Turk and Okifuji (1997) in a heterogeneous sample of chronic pain patients. A later study by

Turk and Okifuji also found no significant sex differences in current use of analgesics or past

treatment in a large sample of chronic pain and cancer-related pain patients (Turk & Okifuji,

1999). Campbell (2002) employed a vignette design to the study of nurses' decision-making

regarding pain management practices. Results of this study indicated that the vast maj ority of

nurses' were not predisposed to administer less opioid medication to hypothetical female post-

surgical patients. Also noteworthy is the fact that the earliest investigations of sex differences in

pain management practice found that males, not females, were at increased risk of receiving sub-

optimal analgesic care (Bond & Pilowsky, 1966; Pilowsky & Bond, 1969), perhaps due to a

culturally sanctioned belief that males should be more tolerant of pain than females (Bond,

1971).

These disparate findings question the reliability of sex as an influence on the prescription

of pain medication. Furthermore, as Robinson and Wise (2003) note, any conclusions that are

drawn from this body of research should be tentative due to methodological issues. Two

additional issues of concern with the entirety of the clinical literature that cannot be overstated

are (1) the absence of any significant manipulation of the independent variables in an

ecologically valid manner, and (2) the noted lack of variability in the sex of the assessor (e.g.,









medical provider). Clearly, then, additional research is needed to clarify the issue of sex and

pharmacologic pain management.

Influence of Age

As noted above, elderly individuals manifest high rates of both acute (e.g., post-surgical)

and chronic (e.g., arthritis) pain conditions. Although there appears to be some evidence of age-

related changes in pain perception, such as declines in endogenous analgesic systems (Edwards,

Fillingim, & Ness, 2003) and altered transmission along A-delta and C nerve fibers (Chakour,

Gibson, Bradbeer, & Helme, 1996; Helme & Gibson, 1997), the effects of these changes on the

experience of pain remain unclear. Results of laboratory-based studies comparing pain threshold

and tolerance across age groups are mixed. This has led some to question the clinical

significance of age-related changes in pain perception (Ferrell, 2003; Harkins, 1996).

Despite the aforementioned lack of empirical consistency, clinical practice guidelines for

pain management assert that elderly patients are at risk of being under-treated for pain (Agency

for Health Care Policy and Research [AHCPR], 1994). As previously noted, the consequences of

uncontrolled pain are considerable. These consequences are heightened in the older adult

population (American Geriatrics Society, 2002). The elderly undergo surgery four times more

often than other age groups (Rooke, Reves, & Rosow, 2002), and greater than half report

inadequate post-operative pain relief (Karani & Meier, 2004). Results of a study by Oberle and

colleagues (1990) indicated that, compared to younger patients with similar reports of pain

intensity, elderly patients received smaller amounts of analgesics following surgery. Results of a

vignette study also indicated that at least some health care providers use age as a significant cue

in the decision making process regarding use of pain medication, such that some nurses were

predisposed to administer less medication to older patients (Campbell, 2002). These findings

hold increased significance when considered in light of empirical evidence that older patients do









not self-administer fewer analgesics than other patient groups (Morgan & Puder, 1989; Owen,

Szekeley, Plummer, Cushnie, & Mather, 1989); although, contradictory findings have been

reported (Gagliese, Jackson, Ritvo, Wowk, & Katz, 2000; Gagliese & Katz, 2003). The literature

concerning management of chronic pain in elderly individuals is relatively small. The use of

opioid drugs for chronic, non-malignant pain is controversial in general, but they are likely

underutilized in treatment of the elderly (Popp & Portenoy, 1996). The literature provides some

support for this contention (AGS, 2002; Auret & Schug, 2005; Ferrell, Ferrell, & Rivera, 1995).

Several possible explanations for these results are available. Age-related physiologic

changes in response to opioids (e.g., increased risk of organ toxicity, increased sensitivity to

medication effects) may induce caution in health care providers in administering such

medications to older individuals in pain. Although these changes certainly complicate the use of

analgesics in these patients, the application of ageist stereotypes to the decision making process -

which the literature suggests exists to some degree is not supported. As reviewed earlier, pain

assessment in elderly patients may be complicated by many factors (e.g., cognitive impairment,

provider attitudes), which has clear implications for pain management. Not only can cognitive

impairment negatively affect pain assessment but it can also be aggravated by both post-

operative pain and the medications used to treat this pain (Montamat, Cusack, & Vestal, 1989;

Moore & O'Keefe, 1999). Effects of these complications are seen in the research demonstrating

that cognitive impairment strongly influences the amount of analgesic medication that medical

providers administer to older patients following trauma or in the post-operative period (Foster,

Pardiwala, & Calthorpe, 2000; Feldt, Ryden, & Miles, 1998; Morrison et al., 2003), as well as in

the nursing home environment (Horgas & Tsai, 1998).









Influence of Race/Ethnicity

In contrast to the relatively small and inconsistent literature concerning race/ethnic

influences on the pain assessment process, there is considerable empirical support for the

assertion that race/ethnicity plays an important role in the treatment of acute and chronic pain.

Race/ethnic disparities in pain management have been reported across a range of conditions.

Racial/ethnic disparities in Emergency Department (ED) pain management have been reported in

several studies. In a series of retrospective studies, Todd and colleagues (1993, 2000) found that

Hispanic and African-American patients were more likely than Whites to receive no pain

medication upon admission to the ED with isolated long bone fractures; these disparities were

not due to ethnic differences in physician pain assessment (Todd et al., 1994). More recently,

Tamayo-Sarver, Hinze, Cydulka, and Baker (2003b) found that African-American migraine and

back pain ED patients were less likely to be prescribed opioids than similar White patients.

Importantly, this disparity was greatest for conditions with fewer obj ective findings (e.g.,

migraine), which presumably permitted non-medical factors, such as race/ethnicity, to play a

larger role in medical decision-making.

Management of post-operative and back pain also evinces racial/ethnic disparities. A

retrospective analysis of post-surgical pain management practices indicated that White patients

consistently received higher doses of analgesics than African-American and Hispanic patients;

these differences persisted after controlling for relevant demographic and clinical variables (Ng,

Dimsdale, Shragg, & Deutsch, 1996b). A follow-up investigation by these same researchers

found that White patients were prescribed a larger amount of patient-controlled analgesia for

post-operative pain than Hispanic patients, and African-American patients were prescribed a

larger amount than Hispanic and Asian patients (Ng, Dimsdale, Rollnik, & Shapiro, 1996a).

Again, these disparities remained significant after controlling for potential confounds.









Racial/ethnic disparities in the management of cancer and HIV/AID S-related pain have

also been documented. Cleeland and colleagues (1994), in a multicenter study, found that

outpatients of cancer clinics that primarily serve ethnic and racial minorities were three times

more likely to be undermedicated with analgesics than were patients in other settings. The

percentage of patients indicating inadequate analgesia was significantly higher in community

clinical oncology programs that treated predominantly African-American and Hispanic patients

than in academic medical cancer centers and community-based hospitals and practices.

Furthermore, African-American and Hispanic patients were more likely than non-minority

patients to receive inadequate pain management in all settings. In a subsequent investigation,

these researchers found that patients treated in settings that primarily serviced African-

Americans, Hispanics, or both were more likely to receive inadequate analgesia than patients

treated in non-minority community treatment settings (Cleeland et al., 1997). Bernabei and

colleagues (1998) found that (1) elderly African-American and Hispanic cancer patients were

less likely to have their pain recorded compared to Whites, and (2) minority nursing home cancer

patients were more likely to have received no analgesia. Specifically, African-Americans had a

63% increased probability of having their pain untreated compared to White patients. Similar

disparities were observed for other racial and ethnic groups, although small sample sizes

precluded detailed analyses of these disparities. Similar results were reported by the Office of

Minority Health; 62% of cancer patients at medical facilities serving primarily African-American

patients and 82% of cancer patients at medical facilities serving primarily Hispanic patients were

prescribed inadequate analgesic medication (Ross, 2000). Less dramatic but still noteworthy,

Anderson and colleagues (2000) found that approximately one-third of African-American cancer

patients received pain medications of insufficient strength to adequately manage their pain. In the









HIV/AIDS literature, differences in pain treatment have been found between members of racial

minority groups and Whites, with minority patients receiving less adequate pain management

(Sambamoorthi et al., 2000).

Although the evidence for racial/ethnic disparities in the treatment of pain appears

overwhelming, contradictory findings have been reported. Karpman, Del Mar, & Bay (1996)

attempted to replicate the findings of Todd et al. (1993) and determine the existence of a

relationship between patient race/ethnicity and the amount of analgesia administered to reduce

pain from a long bone fracture. In contrast to the earlier study by Todd et al. (1993), no

differences between Hispanic and White patients were found in terms of pharmacologic pain

management practice for fracture reduction. A more recent retrospective study also sought to

investigate the influence of patient race/ethnicity on decision making for pain management

following bone fracture (Fuentes, Kohn, & Neighbor, 2002). Consistent with Karpman and

colleagues (1996), no differences in analgesic treatment practices for White, African-American,

Hispanic, or Asian patients were noted. Bartfield and colleagues (1997) conducted a prospective

study of adult patients treated for non-traumatic low back pain to determine the influence of

physicians' impression of patients' race/ethnicity on analgesic prescription practices. Results

indicated that only patient pain, not race/ethnicity, influenced analgesic administration. Two

recent vignette studies also found that race/ethnicity of hypothetical patients did not influence

analgesic practice among physicians and nurses (Campbell, 2002; Tamayo-Sarver et al., 2003a).

When considered in its entirety, the literature indicates that patient race/ethnicity is an

important variable in the treatment of pain. Although not perfectly consistent, this literature

demonstrates that African-Americans and Hispanics are more likely to be under-treated for pain

than their White counterparts. Furthermore, such disparities were found in diverse medical









facilities and geographic locations. These disparities may result from many factors, including

communication difficulties, differential expression and manifestation of pain, differential

preferences and expectations for treatment, and frank provider racism. Continued effort to

elucidate treatment disparities in pain management practices and the reasons behind them is

needed to improve the treatment of pain in all people.

Limitations of the Assessment and Treatment Literature

Although the literature regarding the issues outlined above has expanded and improved in

recent years, conspicuous limitations and gaps remain. A primary issue is methodological. The

two principal approaches to studying pain assessment and treatment are the retrospective and

vignette designs. Retrospective designs have generally taken the form of chart reviews in which

patient medical records are reviewed to determine if patient demographic factors are related to

medical assessment and/or treatment. These designs are fraught with problems that make it

difficult to test hypotheses and draw firm conclusions. Specifically, they preclude any

manipulation of the independent variables of interest (e.g., patient demographics) and limit the

analysis of potential confounds. Additionally, pain report is often not documented in patient

charts (Calderone, 1990; McDonald, 1994; Ng et al., 1996), placing further constraints on the

applicability of retrospective designs to pain research in this context. A more methodologically

sound approach is the vignette study. These designs typically involve the reading of a

hypothetical patient' s "file" by the medical provider, after which the provider answers a series of

questions regarding the patient' s pain level and the medication administration they (provider)

would endorse. Although these designs permit greater control in the manipulation of variables,

they suffer from low external validity and high task transparency/social desirability.

The lack of variability in the demographic characteristics of the health care provider is an

additional limitation. As previously noted, the medical field has seen an expansion of both sex









and racial/ethnic diversity among providers (AMA, 2005; Girard, 2003); however, the vast

maj ority of participants in research investigations to date have been Caucasian and of the

stereotypic sex (i.e., male physicians and female nurses). Not only may patient-provider

congruence on these characteristics influence the medical encounter (Anderson et al., 2003), but

demographically diverse providers may approach clinical problems (e.g., pain) and their

assessment and management in systematically different ways due to the influence of culturally-

sanctioned attitudes and behaviors regarding health.

The literature to date, both in the laboratory and the medical setting, has soundly

demonstrated the importance of investigating the factors that influence medical assessments and

decisions, especially regarding patient pain and analgesic medication administration. However, a

creative, more methodologically sound research design is now needed to probe those questions

that remain unanswered and re-evaluate the conclusions that have been drawn thus far. The

proposed study aims to take this next logical step in the literature regarding pain assessment and

management by health care providers.

Facial Expression of Pain

Patient self-report is the gold standard of pain assessment and typically takes the form of a

verbal response to a pain-related inquiry by the health care provider. Self-report of pain may also

be obtained through response to items from a questionnaire and/or one of many rating scales

(e.g., VAS, NRS). Nonverbal expressions of pain offer a promising adjunct to these self-report

indices (Craig & Prkachin, 1983) and are likely to impact on the pain assessment and treatment

process. In fact, observers generally assign greater weight to nonverbal expressions of pain than

self-report (Craig, 1992; Poole & Craig, 1992). Among the many variants of nonverbal pain

behaviors, facial expressions have been the subj ect of much empirical work. The foundation of

this work is largely provided by Paul Ekman and his colleagues who have demonstrated the









existence of distinct facial expressions representing fundamental emotional states (Ekman,

1992). These states can be accurately detected by observers on the basis of specific facial cues

(Ekman & Friesen 1969b; Ekman, Friesen, & Ellsworth, 1983). Kenneth Craig and his

colleagues extended this line of research to the field of pain in the 1980s, which is not to say that

facial expressions of pain were ignored until only recently. In fact, Darwin (1872/1965)

commented on specific mouth and eye movements that he considered characteristic of the human

expression of pain. Although these facial features did not hold up to later empirical scrutiny, the

notion that the facial expression of pain could be quantified by analysis of specific movements of

facial muscles portended a field of inquiry that did not come to fruition until a full century later.

This field of inquiry has been aided in large part by development of technologies capable

of capturing specific morphological features of distinct facial expressions. The Facial Action

Coding System (FACS; Ekman & Friesen, 1978) is the primary such technology. The FACS is

an obj ective, anatomically-based system that permits a full description of the basic units of facial

movement associated with private experience, including pain. Forty-four different action units

(AUs) scored on a 5-point intensity scale have been identified, which represent the minimal units

of facial activity that are anatomically separate and visually distinguishable. Core action units

representing the facial expression of pain in adults are: brow lowered, cheek raised and lid

tightened, nose wrinkled and upper lip raised, and eye closure (Craig, Prkachin, & Grunau, 1992;

Prkachin, 1992b). Although the associated changes in facial musculature for pain and other

expressions occur along continuous dimensions, these expressions are perceived in a

categorical manner (Etcoff & Magee, 1992; Young et al., 1997). Importantly, the pain expression

is relatively specific to pain, as it can be differentiated from other negative subjective states, such









as disgust, fear, anger and sadness (LeResche, 1982; LeResche & Dworkin, 1988; Hale &

Hadjistavropoulos, 1997).

Following identification of the characteristic facial expression of pain, scientists turned to

investigations of the developmental and cultural stability of this expression, as well as to

investigations in diverse experimental and clinical contexts. A specific facial expression of pain

appears to be present from an early age. Infants from 25 weeks gestation show a characteristic

pain face (Craig, Whitfield, Grunau, Linton, & Hadjistavropoulos, 1993; Grunau & Craig, 1987;

Lilley, Craig, & Grunau, 1996; Stevens, Johnston, & Horton, 1994). Strong consistencies in the

morphology of facial expressions of pain have been observed from birth through old age;

however it is important to note that these expressions are subj ect to environmental pressures,

particularly those related to sociocultural norms and immediate context (Craig, 1980). In contrast

to the sizeable literature on developmental aspects of the pain expression, little cross-cultural

investigations have been conducted. This is a conspicuous gap in the literature given that pain

behaviors may vary dramatically both between and within cultures (Goldberg & Remy-St. Louis,

1998). Cross-cultural studies of facial expression of emotions have been conducted (Ekman &

Friesen, 1971; Ekman et al., 1987); however, the extent to which these studies generalize to pain

is not clear. Unfortunately, this is a difficult area of inquiry due to methodological and

interpretation constraints (Ekman, 1994; Fridlund, 1994; Haidt & Keltner, 1999; Russell, 1994,

1995), but one that is in need of development.

The facial expression of pain across different experimental pain stimuli (Prkachin, 1992b)

and clinical pain conditions (Craig, Hyde, & Patrick, 1991; Hadjistavropoulos & Craig, 1994;

LeResche, 1982; LeResche & Dworkin, 1988; Prkachin & Mercer, 1989) has been investigated

and appears to be relatively constant. Furthermore, the magnitude of facial expression has been









shown to increase in relation to exacerbations of clinical pain intensity and to be related to

several indices of clinical pain severity (Craig et al., 1991; Hadjistavropoulos, LaChapelle,

Hadjistavropoulos, Green, & Asmundson, 2002; LeResche & Dworkin, 1988; Prkachin, Berzins,

& Mercer, 1994; Prkachin & Mercer, 1989).

The complement to a distinctive facial expression of pain is the ability of others to detect

it. As noted above, observers can reliably distinguish the facial expression of pain from that of

other subj ective states. Research indicates that the facial cues inherent in the expression of pain

are used consistently by observers to judge pain in adults and children (Craig et al., 1991; Watt-

Watson, Evernden, & Lawson, 1990); however, the accuracy of these judgments is inconsistent

(see below). Facial expressions of pain make substantial contributions to observer ratings of

others' pain (Ahles et al., 1990; Hale & Hadjistavropoulos, 1997), even when a contradictory

verbal report of the absence of pain is presented (Poole & Craig, 1992). Caregivers of the

profoundly cognitively impaired have been noted to rely heavily on facial expressions as an

index of experienced pain (LaChapelle, Hadjistavropoulos, & Craig, 1999; van Dongen, Abu-

Saad, & Hamers, 1999).

The overall literature concerning observer accuracy of pain estimation based on facial

expressiveness is relatively small, particularly when health care providers' estimations are the

target of investigation. Laypersons and providers are generally accurate in judging the presence

or absence of pain based on facial expressions of the ob served person (Breau et al., 2001;

Goodenough et al., 1997; Lilley et al., 1996; Lindh, Wiklund, Sandman, & Hakansson, 1997;

Prkachin et al., 1994). However, when judgments involve pain severity and not merely the

presence or absence of pain, concordance between observer and observed pain ratings decreases.

For example, Prkachin and colleagues (1994) videotaped the faces of patients experiencing









shoulder pain and found that untrained observers of these tapes underestimated patients' pain by

as much as 80%. In a more recent study, Prkachin, Solomon, Hwang, and Mercer (2001)

investigated the influence of pain familiarity on accuracy of pain judgments. Three groups of

participants laypersonss with a family history of pain conditions, laypersons with no family

history of pain conditions, and health care providers) viewed videotapes of patients undergoing a

painful medical procedure, after which they provided pain assessments of these patients.

Patients' pain was underestimated by each group of participants, but to varying degrees.

Participants with a family history of pain attributed greater pain to the patients than did those

with no such history. Furthermore, health care providers (physical and occupational therapists)

attributed the least amount of pain to patients. These data suggest that personal and professional

experience with pain may influence the assessment of pain in others. In a study by Goodenough

and colleagues (1997), pediatric nurses indicated that they relied heavily on facial cues when

making global estimates of pain. However, results revealed a significant discrepancy between

children' s self-report of pain (visual analogue toy) and the nurses' judgment, such that nurses'

ratings of patient pain were consistently lower than the patients' ratings. Although this literature

is small, it is generally consistent with research reviewed earlier in which provider

underestimation of patient pain appears to be the rule rather than the exception.

Lens Model Design

As noted above, methodological shortcomings are characteristic of the extant literature

concerning pain assessment and treatment. The proposed study will attempt to address these

shortcomings by employing a lens model design. The lens model is an analogue method for

capturing how individuals use information in their environment to form judgments. It is both a

theoretical model of how individuals use information to make judgments, and an experimental

paradigm for studying judgment processes and outcomes (Beal, Gillis, & Stewart, 1978). The









lens model was originally formulated by Egon Brunswick in the 1950s and later refined by

Kenneth Hammond within his Social Judgment Theory (Cooksey, 1996).

Inherent in the lens model approach is the assumption that judgment processes are

contextually determined. That is, an individual's judgment is determined based on his/her

attention to and weighting of the information (cues) available in the immediate environment. In

lens model applications, individuals are presented a series of profiles containing cues that may be

used to form a judgment. The profiles depict cases or situational contexts for the individual to

process, and each contains a unique combination of cues. The outcome of the judgment process

for each profile is obtained using a quantifiable response mode, such as NRSs or VASs. Policy

capturing occurs at the idiographic level utilizing multiple regression procedures. A linear

equation is produced that optimally weights each cue in terms of its predictive contribution to the

judgments. Once an individual's judgment policy has been captured, a coefficient of multiple

determination (R2) can be generated, which represents the proportion of the variance in

judgments accounted for by the linear model of the individual. This model also permits data

aggregation for group analyses.

Rationale

As reviewed above, there are varying degrees of evidence that an individual's demographic

characteristics namely, sex, age, and race influence observers' assessment and treatment (in

the case of medical providers) of pain. The vignette- and retrospective-based methodologies

most frequently employed in such investigations impose constraints on the research questions

that could be addressed and the conclusions that have been yielded. An innovative research

design that capitalizes on the advantages of these methodologies while limiting their

disadvantages may further our understanding of these complex issues. Further, a more detailed

analysis of the clinical decision making process itself than has heretofore been conducted would









be a positive direction for this line of research. Such an analysis would extend past the practice

of mere examination of the end result i.e., the decision product and instead permit

investigation of the process that precedes the result. Only through analysis of the process can we

begin to understand where biases and knowledge deficits infiltrate clinical decision-making.

Investigations of this type have the potential to inform future intervention efforts aimed at

rectifying such problems. In addition, to date, little work has examined the interaction of the

characteristics of both the observer and the observed in this context. This is largely due to the

difficulty in securing participants of sufficient variability to conduct adequately powered

analyses. Thus, investigations like the current one that particularly target providers of variable

personal and professional characteristics are needed.









CHAPTER 2
IVETHOD S

Participants

All participants were at least 18 years of age and a licensed practicing Registered Nurse

(RN). Students and those with advanced nursing degrees were included if they met the

aforementioned criteria. Recruitment of participants occurred at the local and national level.

Local recruitment strategies included presentations at class lectures, advertisements displayed in

local hospitals and clinics, and presentations at local and state association meetings. National

recruitment occurred via nursing mailing lists and email listserys, and through attendance at

national meetings. It was expected that this approach would maximize the demographic and

clinical diversity of practicing professionals. Continuing education credits for the debriefing

portion of this study or financial compensation served as incentives for participation.

The current study was powered for the idiographic analyses of the lens model approach.

Task sensitivity, the principal concern of lens model designs, is primarily a function of the ratio

of profiles to cues. The smallest recommended profile-to-cue ratio is 5:1, but a 10: 1 ratio may be

preferred given logistical feasibility (Cooksey, 1996). Idiographic power of this study was

maximized by employing a ratio that exceeded the acceptable 5:1 ratio. The 10:1 ratio would

likely have imposed undue burden on study participants through the creation of a large number

of profiles and, thus, was not adopted. This study investigated 4 contextual cues (age, race, sex,

and pain expression) and used a total of 32 profiles, which is a profile-to-cue ratio of 8:1. This

ratio permitted each possible cue combination to be presented twice, which further enhanced

statistical power. It was expected that this ratio would ensure adequately powered nomothetic

analyses when the idiographic data were aggregated. Lens model designs that employ a sufficient

profile-to-cue ratio have enhanced power at the nomothetic level due to greater reliability of each










individual's data as a result of multiple observations. Thus, policy-capturing investigations like

the current study can achieve adequate power with a smaller sample size than traditional research

designs (Cooksey, 1996). Given the methodological uniqueness of the proposed study, it was

difficult to conduct a precise traditional power analysis at the nomothetic level. However, based

on a modified power analysis using Power Analysis and' Sample Size (PASS) software, the results

of a previous study (Campbell, 2002) that most closely resembles the current one, and the

literature reviewed above, a total of 50 participants were planned for recruitment.

Measures

Demographics Questionnaire

A demographics questionnaire elicited information pertaining to participant sex, age,

race/ethnicity, years of professional nursing experience, and past practice settings and clinical

specialties.

Gender Role Expectations of Pain

The GREP (Robinson et al., 2001) is comprised of 12 visual analog scales (VAS) that

assess an individual's view of the typical man and woman with respect to pain sensitivity, pain

endurance, and willingness to report pain. It also assesses the individual's personal attribution of

his or her pain sensitivity, pain endurance, and willingness to report pain relative to the typical

man and woman. Psychometric properties of the GREP are sound. The factor structure is

consistent with the theoretical formulation of the scales and accounts for 76% of the variance in

scores. The GREP has good test-retest reliability with individual item correlations ranging from

.53 to .93. High correlations (-.71 to -.81) between individual items reflecting the opposite

gender role (i.e., typical male endurance of pain correlated with typical female endurance)

demonstrates internal consistency. Finally, sex differences in the endorsement of items on the

GREP are large, with the largest differences (46% of variance) shown for "willingness to report










pain" items. These differences provide evidence for the construct validity of the measure

(Robinson et al., 2001). The GREP has also been demonstrated to be a significant predictor of

experimental pain ratings in undergraduate men and women, accounting for a significant

proportion of the sex differences in pain report (Wise et al., 2002). Consistent with previous

research (Robinson and Wise, 2003), two theoretically important items from the GREP were

included in the subsequent analyses to determine if gender stereotypes about "endurance of pain"

and "willingness to report pain" influence clinical decisions regarding pain assessment and

management practices.

Lens Model Design

The current study employed a lens model design, an analogue method for capturing how

individuals use information in their environment to form judgments. The lens model serves as a

theoretical model of how individuals use information to make judgments, and as an experimental

paradigm for studying judgment processes and outcomes (Beal et al., 1978). The lens model

approach is based on the assumption that the immediate environmental context i.e., the cues

available to the individual's perceptual faculties influences an individual' judgment process.

Empirical applications of this approach typically consist of a series of cue-containing profies

presented to a study participant, about which the participant forms a judgment. This judgment is

recorded via a quantifiable response mode, such as a VAS.

In the current study, the outcome of the judgment process was each participant' s

assessment ratings of pain and mood, as well as his/her decisions regarding pain management

practices. The contextual cues of interest that vary systematically across clinical profiles are

patient age, sex, race, and expression of pain.









Profiles

Each profie consists of a vignette and video clip. The vignettes contain patient clinical

information indicating the status of the patient, pain complaint (duration and location), and

prescription medication orders. The maj ority of the patient clinical information is included only

to enhance task familiarity and ecological validity; this information has minimal variability and

is within normal limits. The remaining information is used to provide the participants with the

context in which they are to make assessment and treatment decisions.

The video clips were generated with People Putty software by Haptek Incorporated.

People Putty is a technology that permits the user to develop virtual characters with a variety of

features. Standard characters are available for use; however, users may also upload digital

picture Hiles of actual people and program these files into the existing software. Various

demographic features (e.g., sex, age, and race) can be manipulated to create a diverse array of

characters. A particularly innovative and desirable feature of this technology is the ability to

manipulate the facial expressions of characters. Users can manipulate specific facial features in

order to achieve a desired expression. The specific features of an empirically-validated pain

expression brow lowered, cheek raised and lid tightened, nose wrinkled and upper lip raised,

and eye closure can be altered to represent varying degrees of pain expressivity. Furthermore,

these expressions can then be held constant and applied to other characters of different

characteristics. For example, the characteristic features of a "high" pain face can be equally

applied to both a young, African-American male and elderly, Caucasian female. This feature of

People Putty permits a level of experimental control that is lacking in retrospective-based

research, and permits a level of ecological validity that is lacking in vignette-based research. In

this manner, the current design sought to maximize the advantages of these approaches while

minimizing their disadvantages. The control afforded by the virtual patient technology is also










greater than that available through the use of actual persons who have been attempted to be

equated on demographic characteristics and trained in the display of facial expressions of pain.

Another important advantage over the use of trained actors is that the virtual patient technology

eliminates from the development of the stimuli the very biases this study is intending to

investigate.

Profiles were presented randomly to control for order effects. Each profile contains four

cues: sex (male, female), age (young adult, old adult), race (Caucasian, African-American), and

pain expression (low, high). The cues of patient sex, age, and race were available to participants

from the video clips. Pain intensity level was represented by the facial expressions of the

characters. As noted above, these facial expressions were generated through manipulation of the

specific features characteristic of the pain face. The FACS was used to direct the creation of

these expressions. A total of 16 unique scenarios were created representing all possible cue

combinations. In the current study, in order to achieve maximal task sensitivity, each participant

viewed each possible cue combination twice, for a total of 32 profiles.

Judgments

Four assessment ratings were obtained for each profile presented. Participants rated each

virtual patient' s level of pain intensity and pain unpleasantness, as well as their level of positive

and negative mood. Pain assessment ratings were recorded on separate VASs with endpoints at

no pain sensation and most intense pain sensation imaginable for pain intensity, and not at all

unpleasant and most unpleasant imaginable for pain unpleasantness. Mood assessment ratings

were recorded on VASs with endpoints at neutral and most positive imaginable for positive

mood, and neutral and most negative imaginable for negative mood. Four treatment ratings were

also obtained for each profile: (1) likelihood of administering a non-opioid analgesic within

prescribed dosage, (2) likelihood of administering an opioid analgesic within prescribed dosage,










(3) likelihood of recommending a change in non-opioid analgesia to achieve better pain control,

and (4) likelihood of recommending a change in opioid analgesia to achieve better pain control.

Separate VASs were used for each rating, with endpoints at not at all likely and complete

certainty. The VASs for assessment and treatment ratings consist of computerized horizontal

lines anchored by their corresponding endpoint descriptors. Participants used a "slider" to

indicate the point that best represents their assessment and treatment ratings. The distance from

the left-most endpoint to the point indicated by the participant represents their ratings.

Procedure

A WEB-based delivery model was used for the current study. Each participant was asked

to read an informed consent that included a description of the study, time required to complete

the study, and compensation for their time if they decided to participate. Participants provided

electronic consent if they agreed to participate. After providing consent, participants completed

the demographics questionnaire. The order of the GREP and patient profie administration was

counterbalanced and followed the completion of the demographics questionnaire. The following

procedure was used for all administrations of the patient profies: (1) participants read the

clinical information and view the video simultaneously; (2) participants complete questions that

ask them to provide pain and mood ratings using electronic VASs, and rate the likelihood of

carrying out pain management practices. Prior to the patient profile portion of the study,

participants read an "instructions" document that informed them about how to approach the task

and how to use the electronic VASs to give pain, mood, and treatment ratings. Participants are

instructed to fully complete the questions for each profie and are not permitted to return to

previously completed profies. To maximize compliance with instructions and provide answers

to frequently posed questions, a help menu was provided and accessible at all times.









Following completion of the study, participants were administered a short task-validity

questionnaire that asked them to guess at the purposes) and/or hypothesis(es) of the study. They

were also asked about what information they used when making their assessment and treatment

ratings. Participants were then fully debriefed regarding the variables of interest and the study

hypotheses. A brief educational tutorial regarding pain practice with sex, age, and ethnically

diverse patients was then provided, after which participants completed a short test of their

knowledge in this area. All data were collected and stored in an electronic database. The time

necessary to complete the study varied between approximately 60 and 90 minutes, and was

primarily a function of individual participant's computer specifications.

Hypotheses

Pain Assessment

1A. There is a discrepancy between the experimental and clinical literature concerning the

effect of patient sex on pain assessment. The Robinson and Wise (2003) hypothesis that gender

role stereotypes will result in observers rating males' pain lower will be supported in the current

study by pain assessment ratings for male virtual patients being reliably lower than those for

female virtual patients. Conversely, the clinical pain literature indicates that females are at

increased risk of having their pain under-assessed. This may be due to provider beliefs about sex

differences in pain perception, the fact that the appraisal of pain may be more difficult for

patients who are not of the same sex (primarily male providers were included in these

investigations), and/or some other as-yet undetermined reason. Support for these results will be

seen in the current study by pain assessment ratings for female virtual patients being reliably

lower than those for male virtual patients.

1B. Elderly patients will be judged to have lower pain intensity and unpleasantness than

young patients.









1C. African-American patients will be judged to have lower pain intensity and

unpleasantness than Caucasian patients.

1D. Patients demonstrating a low facial expression of pain will be judged to have lower

pain intensity and unpleasantness than patients demonstrating a high facial expression of pain.

Treatment with Non-opioid Medication

2A. Patient sex cues will predict likelihood of administering non-opioid medication.

Specifically, providers will be less likely to utilize this treatment modality with female patients

relative to male patients.

2B. Patient age cues will predict likelihood of administering non-opioid medication.

Specifically, providers will be less likely to utilize this treatment modality with elderly patients

relative to younger patients.

2C. Patient race cues will predict likelihood of administering non-opioid medication.

Specifically, providers will be less likely to utilize this treatment modality with African-

American patients relative to Caucasian patients.

2D. Patient pain expression cues will predict likelihood of administering non-opioid

medication. Specifically, providers will be less likely to utilize this treatment modality with

patients demonstrating a low facial expression of pain relative to patients demonstrating a high

facial expression of pain.

Treatment with Opioid Medication

3A. Patient sex cues will predict likelihood of administering opioid medication.

Specifically, providers will be less likely to utilize this treatment modality with female patients

relative to male patients.









3B. Patient age cues will predict likelihood of administering opioid medication.

Specifically, providers will be less likely to utilize this treatment modality with elderly patients

relative to younger patients.

3C. Patient race cues will predict likelihood of administering opioid medication.

Specifically, providers will be less likely to utilize this treatment modality with African-

American patients relative to Caucasian patients.

3D. Patient pain expression cues will predict likelihood of administering opioid

medication. Specifically, providers will be less likely to utilize this treatment modality with

patients demonstrating a low facial expression of pain relative to patients demonstrating a high

facial expression of pain.

Recommendations for Change in Medications

4A. Patient sex cues will predict likelihood of recommending a change in both non-opioid

and opioid medications. Specifically, providers will be less likely to make recommendations on

behalf of female patients relative to male patients.

4B. Patient age cues will predict likelihood of recommending a change in both non-opioid

and opioid medications. Specifically, providers will be less likely to make recommendations on

behalf of elderly patients relative to younger patients.

4C. Patient ethnicity cues will predict likelihood of recommending a change in both non-

opioid and opioid medications. Specifically, providers will be less likely to make

recommendations on behalf of African-American patients relative to Caucasian patients.

4D. Patient pain expression cues will predict likelihood of recommending a change in both

non-opioid and opioid medications. Specifically, providers will be less likely to make

recommendations on behalf of patients demonstrating a low facial expression of pain relative to

patients demonstrating a high facial expression of pain.









Healthcare Provider Characteristics

There are few empirical investigations of the influence of healthcare provider

characteristics on pain assessment and pain management practices. There has also been a lack of

theoretical attention to these issues. Consequently, specific, empirically and/or theoretically

informed hypotheses concerning the influence of provider characteristics on pain assessment and

treatment are not proposed. To the extent that variability in the characteristics of providers who

participate in the current study permits, exploratory analyses will be conducted.

Statistical Analyses

Descriptive statistics were conducted to summarize the demographic and background

characteristics of the sample.

Idiographic

Simultaneous multiple regression equations were generated for each individual to capture

his/her decision making policies. Virtual patient age, race, sex, and pain expression served as

independent variables in each model. Pain and mood assessment ratings, medication-based

treatment ratings, and change-related recommendation ratings were dependent variables in their

respective models. The standardized regression coefficients in each equation represent the weight

of each cue in the formation of the assessment and treatment judgments. This weight represents

the unique contribution and relative importance of each cue in the participant' s clinical decision.

The coefficient of multiple determination (R2) TepfeSents the amount of variance in assessment

and treatment decision policies accounted for by the predictor variables, or the overall function

of the cues in each individual's policy.

Nomothetic

Following idiographic analyses for all participants, descriptive statistics were conducted to

determine: 1) the total number of cues that were significantly weighted at each decision policy;









2) the amount of variance accounted for by each cue in the separate decision policies; 3) the

number of significant overall decision policies; and 4) the average coefficient of determination

for each decision policy. Paired samples t-tests compared ratings within cue for the entire

sample. Finally, Chi-square tests and Analysis of Variance (ANOVA) were conducted to explore

whether participant demographic and professional background characteristics were related to

overall pain assessment and treatment policies and contextual cue utilization.









CHAPTER 3
RESULTS

Participants

Fifty-four nurses participated in this study. Consistent with national data, the vast maj ority

of nurses were female (83%) and self-reported Caucasian (93%). The average age of the sample

was approximately 42 years (SD = 1 1.90). A wide range of geographical locations was

represented, with Florida (n = 23) being the modal state of residence. Twenty-two participants

held an Associate Degree in nursing, whereas 17 matriculated at the Bachelor level and 15 at the

graduate level. At the time of their participation, approximately 72% were not currently enrolled

in an academic program. Of the 15 nurses who were currently students, the majority (n = 11) was

pursuing graduate degrees. Examination of self-reported professional background data indicated

that the average years of nursing experience was approximately 14 (SD = 10.52). The three most

frequently endorsed current practice areas were critical care (n = 22), primary care (n = 16), and

oncology (n = 14). With one exception, all nurses reported experience working in a hospital

setting. Detailed demographic and background information is provided in Table 3-1.

Pain Assessment Policies

Patient sex, race, age, and pain expression were hypothesized to be significant,

independent factors in nurses' assessments of pain intensity and unpleasantness. Specifically, it

was expected that, relative to their within-cue counterparts, lower pain intensity and

unpleasantness ratings would be assigned to patients who were African-American, older, and

displaying high levels of pain expressivity. Competing hypotheses were articulated regarding the

influence of patient sex on pain assessment ratings.










Table 3-1. Demographic and background characteristics of participants
N % of total Mean (SD) Range
Sex
Female 45 83.3
Male 9 16.7
Age (years) 42.02 (11.90) 22 66
Race
Caucasian 50 92.6
African-American 2 3.7
Asian 1 1.9
Other 1 1.9
Nursing education
Associate's degree 22 40.7
Bachelor's degree 17 31.5
Graduate degree 15 27.8
Current educational status
Not enrolled 39 72.2
Enrolled 15 27.8
Nursing experience (years) 14.06 (10.52) 0 37
Practice area
Critical Care 9 16.7
Emergency 7 13.0
Oncology 7 13.0
Medical-Surgical 6 11.1
Internal Medicine 4 7.4
Pediatrics 4 7.4
Primary Care 3 5.6
Ob stetrics 2 3.7
Psychiatry 2 3.7
Hospice 1 1.9
Other 9 16.7
Practice setting*
Hospital 53 98.1
Outpatient facility 16 29.6
Nursing home 8 14.8
Hospice 5 9.3
*Categories are not mutually exclusive.

Pain Intensity

Results indicated that 34 nurses had significant (p < .05) policies for pain intensity

assessment; 5 had policies that approached significance (p < .1). Thirteen of these 39 nurses used

sex as a prominent (p < .1) cue in their policy. Ten gave higher pain intensity ratings for females;

the reverse was true for 3 nurses. Race was a prominent cue in the policies of 8 of these 39










nurses, with 7 more likely to judge higher pain intensity in African-American virtual patients and

1 more likely to judge higher pain intensity in Caucasians. Thirteen nurses used age as a

prominent cue in their pain intensity assessment policies. Twelve were more likely to judge older

virtual patients as experiencing greater pain intensity, whereas the converse was true for 1 nurse.

Finally, with the exception of 1, pain expression was a prominent cue for all nurses, such that

virtual patients displaying high levels of pain expression were judged to be experiencing greater

levels of pain by the participants. Results of idiographic regression analyses for pain intensity

assessments are presented in Table A-1.

Pain Unpleasantness

Similar results were obtained for pain unpleasantness ratings, such that such that 35 nurses

had significant (p < .05) policies, and 2 had policies that approached significance (p < .1).

Examination of the contribution of the specific contextual cues indicated that sex, race, age, and

pain expression were prominent cues in the policies of 13, 8, 13, and 36 of these nurses,

respectively. Eleven of the nurses with a significant sex cue were more inclined to make higher

ratings for female patients; the converse was true for 2 nurses. African-American virtual patients

were assessed to be experiencing more pain unpleasantness by 7 nurses, whereas 1 nurse judged

Caucasian patients to be experiencing more pain. Relative to younger patients, older patients

were judged to be experiencing more pain unpleasantness by 12 nurses; the opposite was true for

1 nurse. Finally, every nurse with a prominent pain expression cue judged those with high

expressivity to be experiencing greater pain unpleasantness than those with low expressivity.

Results of idiographic regression analyses for pain unpleasantness assessments are presented in

Table A-2.









Mood Assessment Policies


Positive Mood

Twenty-three nurses had significant (p < .05) policies for positive mood assessment; 2 had

policies that approached significance (p < .1). The 3 nurses in whom sex played a prominent role

in their positive mood policies each judged male patients to be experiencing greater positive

mood relative to female patients. Six nurses used race as a prominent cue; 4 were more likely to

judge Caucasian patients as having greater positive mood, whereas 2 indicated this for African-

Americans. Thirteen had policies in which age was a prominent cue. Of these 13 nurses, 12

assessed younger virtual patients to be experiencing greater positive mood relative to older

patients. Conversely, 1 nurse assessed older patients to be experiencing greater positive mood.

Twenty-four nurses used pain expression as a prominent cue; all were more likely to assign

greater ratings to patients with low expressivity. Results of idiographic regression analyses for

positive mood assessments are presented in Table A-3.

Negative Mood

Thirty-five nurses had significant (p < .05) policies for negative mood assessment; 3 had

policies that approached significance (p < .1). Regarding sex, 12 nurses had policies in which

this cue was prominent. Ten nurses judged female patients to be experiencing greater negative

mood compared to males, whereas the converse was true for 2 nurses. Of the 8 nurses who used

race as a significant cue, 6 assigned greater negative mood ratings for African-American

patients. The remaining 2 gave greater ratings for Caucasian patients. All 13 nurses with policies

in which age was a prominent cue assessed greater negative mood in older virtual patients

relative to younger patients. Finally, all 34 nurses who used pain expression as a prominent cue

assigned greater negative mood ratings to those with high expressivity. Results of idiographic

regression analyses for negative mood assessments are presented in Table A-4.









Treatment Decision Policies

The patient cues of sex, race, age, and pain expression were each hypothesized to play a

significant, unique role in policies regarding administration of non-opioid and opioid

medications. Specifically, a greater likelihood of medication administration (both non-opioid and

opioid) was expected for patients who were male, Caucasian, younger, and displaying high pain

expressivity.

Non-opioid Medication

Twelve nurses had significant (p < .05) policies for non-opioid treatment; 3 had policies

that approached significance (p < .1). Sex played a prominent role for 2 nurses out of the 15 with

significant overall non-opioid policies. Both of these nurses gave higher ratings to female

patients and, thus, were more likely to engage in this treatment practice with them compared to

male patients. Three nurses used patient race as a prominent cue. African-American patients

were more likely to be administered a non-opioid medication by 2 nurses. One nurse was more

likely to engage in this practice with Caucasian patients. Age was a prominent cue in the policies

of 6 of these nurses. Four were more likely to administer non-opioid medication to younger

patients; 2 were more likely to engage in this treatment practice for older patients. Lastly, pain

expression was a prominent cue in the policies of 13 nurses, with low expressive virtual patients

being more likely to receive non-opioid medication by 7 nurses and high expressive patients

more likely to receive this treatment by 6 nurses. Additional, unplanned analyses were conducted

in response to the disparate directional effect of the pain expression cue between non-opioid and

opioid treatment domains. As noted below, every nurse who significantly weighted patient pain

expression when making opioid treatment ratings used this cue in a similar manner; high

expression patients received higher ratings than low expression patients. Since the direction of

this effect was approximately equal for non-opioid decisions, follow-up analyses tested whether










opioid ratings differed between these two non-opioid groups (i.e., those who used pain

expression in a positive vs. negative way). Results indicated that the two non-opioid groups did

not provide significantly different ratings for high and low expression patients in regards to

opioid treatment [F(1,11) = .39, p > .05]. Idiographic regression results for non-opioid treatment

policies are presented in Table A-5.

Opioid Medication

Twenty-three nurses had significant (p < .05) policies for opioid treatment; 4 had policies

that approached significance (p < .1). Sex was a prominent cue in the opioid treatment policies of

7 nurses. Of these, 6 were more likely to engage in this treatment with female patients; 1 was

more likely to do so with male patients. All 5 nurses with a prominent race cue were more likely

to administer opioid treatment to African-American patients. Of the 9 who used age as a

prominent cue, 8 were more likely to engage in this treatment with older versus younger patients.

The converse was true for 1 nurse. All of the 25 nurses with a prominent pain expressivity cue

were more likely to administer opioid medication to highly expressive patients. Idiographic

regression results for opioid treatment policies are presented in Table A-6.

Recommendation Policies

All four patient cues were hypothesized to exert a significant, independent influence on

policies regarding recommendations for a change in non-opioid and opioid medication.

Specifically, for both classes of medication, it was expected that nurses would be less likely to

make such recommendations for patients who were female, African-American, older, and

displaying high levels of pain expressivity.

Change in Non-opioid Medication

Fifteen nurses had significant (p < .05) policies for recommending a change in non-opioid

treatment; 3 had policies that approached significance (p < .1). Of these 18, sex was a prominent









cue for 3, race was a prominent cue for 7, age was a prominent cue for 3, and pain expression

was a prominent cue for 16. Two nurses were more likely to make recommendations on behalf of

female patients; 1 nurse was more likely to do so on behalf of male patients. Four nurses were

more likely to recommend a change for African-American patients. A recommendation was

more likely for Caucasian patients in 3 nurses. Two nurses were more likely to recommend a

change for younger patients than older, whereas the converse was true for 1 nurse. Finally,

patients with a high level of pain expressivity were more likely to have recommendations made

on their behalf by 13 of the nurses. Three nurses were more likely to recommend a change for

patients with low pain expressions. Table A-7 presents the results of idiographic regression

analyses for policies regarding recommendations of change in non-opioid medication.

Change in Opioid Medication

Sixteen nurses had significant (p < .05) policies for recommending a change in non-opioid

treatment; 6 had policies that approached significance (p < .1). Sex was a prominent cue in the

policies of 3 of these 22. Change recommendations were more likely for female patients among 2

nurses; the converse was true for 1 nurse. All of the 4 that used race as a prominent cue were

more likely to make recommendations for African-American patients relative to Caucasian.

Seven nurses had policies in which age was a prominent cue. Five nurses were more likely to

recommend a change for older patients. Two were more likely to make a recommendation on

behalf of younger patients. Regarding pain expression, all of the 20 nurses with a prominent

expression cue were more likely to recommend a change in opioid treatment for patients

displaying a high pain expression compared to those displaying a low pain expression. Table A-8

presents the results of idiographic regression analyses for policies regarding recommendations of

change in opioid medication.









Number of Significant Cues

Descriptive and frequency data were generated for the total number of cues that were

significantly weighted at each decision policy (Table 3-2). Participants with invariant policies

were excluded from these analyses. On average, a greater number of cues were used for pain and

mood assessment policies (pain intensity: M~= 1.51, SD = 1.01; pain unpleasantness: M~= 1.45,

SD = 1.01; positive mood: M~= 1.27, SD = .88; negative mood: M~= 1.36, SD = 1.00). Decision

policies about non-opioid treatment had the least number of significant cues (non-opioid

treatment: M~= .66, SD = .80; non-opioid recommendation: M~= .76, SD = .89). Results of

frequency analyses indicated that, for pain and mood assessments, the maj ority of participants

significantly weighted one or two cues in their policies. For decisions (treatment and change)

regarding non-opioid medication, at least half of participants did not have a significant cue in

their policies, and over 75% used 1 or fewer cues. Approximately 70% of nurses had an opioid

treatment policy with 1 or 2 significant cues, whereas the maj ority of nurses significantly

weighted 1 or fewer cues in their decisions about recommending a change in opioid medication.

Significance of Contextual Cues

In order to quantify the amount of variance accounted for by each cue in the various

decision policies, individual standardized regression coefficients for each cue within each policy

across nurse were squared. Results of these calculations indicated that sex, race, age, and

expression cues accounted for as much as 13%, 15%, 26% and 77%, respectively, of the variance

in policies for pain intensity assessments and 14%, 13%, 28%, and 79%, respectively, of the

variance in policies for pain unpleasantness assessments. In regards to mood assessments, these

cues accounted for as much as 15%, 15%, 22%, and 62%, respectively, of the variance in

positive mood policies and 20%, 14%, 27%, and 81%, respectively, of the variance in negative

mood policies. Examination of the regression coefficients for medication-related treatment









Table 3-2. Number of significant cues at each policy


Number of significant cues
1 2 3
N(%) N(%) N(%)
21(39.6) 14(26.4) 9(17.0)

21(39.6) 14(26.4) 8(15.1)

23(46.9) 12(24.5) 5(10.2)

22(41.5) 15(28.3) 4(7.5)

13(26.0) 10(20.0) 0(0.0)

22(43.1) 14(27.5) 3(5.9)

14(28.0) 9(18.0) 2(4.0)

24(46.2) 10(19.2) 2(3.8)


4
N(%)
1(1.9)

1(1.9)

0(0)

2(3.8)

0(0)

0(0)

0(0)

0(0)


Decision policy
Pain intensity
assessment
Pain unpleasantness
assessment
Positive mood
assessment
Negative mood
assessment
Non-opioid
treatment
Opioid
treatment
ul Recommendation:
non-opioid
Recommendation:
opioid


Min
0

0

0

0

0

0

0

0


N(%)
8(15.1)

9(17.0)

9(18.4)

10(18.9)

27(54.0)

12(23.5)

25(50.0)

16(30.8)


Mean
1.51

1.45

1.27

1.36

.66

1.16

.76

.96


SD
1.01

1.01

.88

1.00

.80

.86

.89

.82


Max
4

4

3

4

2

3

3

3










policies revealed that sex, race, age, and expression cues accounted for a maximum of 21%,

30%, 26%, and 38%, respectively, of the variance in non-opioid decisions and 17%, 22%, 14%,

and 85%, respectively, of the variance in opioid decisions. Finally, similar calculations were

made for the two recommendation policies. Results indicated that up to 25%, 13%, 22%, and

34% of the variance in recommendations for a change in non-opioid treatment and 14%, 15%,

17%, and 68% of the variance in recommendations for a change in opioid treatment were

accounted for by the patient cues of sex, race, age, and expression, respectively. Table 3-3

contains detailed results of these calculations.

Table 3-3. Variance in decision policies explained by contextual cues
Sex Race Age Pain expression
Decision policy Mean (SD) Mean (SD) Mean (SD) Mean (SD)
Range Range Range Range
Pain intensity assessment .03(.04) .03(.04) .04(.05) .29(.22)
.00-.13 .00-.15 .00-.26 .00-.77
Pain unpleasantness assessment .04(.04) .03(.03) .05(.06) .30(.23)
.00-.14 .00-.13 .00-.28 .00-.79
Positive mood assessment .03(. 04) .03(.04) .06(.06) .7.6
.00-.15 .00-.15 .00-.22 .00-.62
Negative mood assessment .04(.05) .03(.05) .04(.05) .26(.22)
.00-.20 .00-.14 .00-.27 .00-.81
Non-opioid treatment .03(. 04) .04(.05) .04(.05) .10(.11)
.00-.21 .00-.30 .00-.26 .00-.38
Opioid treatment .04(. 04) .04(.05) .04(.04) .23(.23)
.00-.17 .00-.22 .00-.14 .00-.85
Recommendation: non-opioid .03(.05) .04(.03) .04(.05) .2.5
.00-.25 .00-.13 .00-.22 .00-.34
Recommendation: opioid .03(.03) .03(.04) .03(.04) .7.9
.00-.14 .00-.15 .00-.17 .00-.68
Note: Values represent squared standardized regression coefficients.

Number and Significance of Overall Policies

Descriptive and frequency data were generated for the entire sample at the level of overall

policy (R2). Out of eight total assessment and treatment decisions, participants had an average of

3.56 (SD = 2.77; Range: 0-8) significant (p < .1) decision policies. The modal number of

significant policies was 1 (n = 10), with the next most frequent being 8 (n = 8), 3 (n = 7), 4










(n = 7), and 0 (n = 7) significant policies. Six participants had 2 significant policies across the

study; 5 nurses had 7 significant policies, with the remaining nurses having 6 (n = 2) and 5

(n = 2) significant policies. These data indicate the presence of variability across the assessment

and treatment ratings in terms of which participants had significant overall decision policies.

Descriptive data were generated for the individual decision policies across participants.

As can be seen in Table 3-4, there is wide variability in overall R2S, both within and between

policies. On average, a greater amount of total variance was accounted for in pain assessment

policies (pain intensity: M~= .40; pain unpleasantness: M~= .41) than policies pertaining to

treatment and recommendation decisions. The least amount of variance (M~= .21) was accounted

for in decisions about non-opioid treatment.

Table 3-4. Descriptive data on overall policy capturing
Decision policy Mean SD Min Max
Pain intensity assessment .40 .22 .02 .82
Pain unpleasantness assessment .41 .24 .03 .84
Positive mood assessment .29 .17 .02 .70
Negative mood assessment .37 .22 .01 .83
Non-opioid treatment .21 .12 .05 .51
Opioid treatment .34 .22 .03 .86
Recommendation: non-opioid .23 .16 .01 .62
Recommendation: opioid .26 .19 .00 .73
Note: Values represent coefficients of determination (R').

Within-cue Comparisons

For each nurse, average assessment and treatment ratings were calculated across virtual

patients at each level of cue (Table 3-5). Paired samples t-tests were then used to compare ratings

within cue for the entire sample.

Pain Assessment

For pain intensity and unpleasantness ratings, significant differences were present within

each cue. Nurses assessed female patients to be experiencing greater pain intensity [t(46) = -3.83,

p < .001, d= .76] and unpleasantness [t(46) = -4.22, p < .001, d= .90] than male patients.









Table 3-5. Means and standard deviations for ratings within cue


Sex Age
Male Female Young O


Race
Caucasian African-


Expression
Low High


American
45.29
(20.23)
46.40
(20.29)
13.66
(11.93)
40.92
(21.31)
61.22
(34.24)
58.98
(28.72)
40.41
(31.57)
40.25
(30.64)


Pain intensity
assessment
Pain unpleasantness
assessment
Positive mood
assessment
Negative mood
assessment
Non-opioid
treatment
Opioid treatment

Recommendation:
non-opioid
Recommendation:
opioid


41.95
(20.97)
43.05
(21.41)
14.37
(12.96)
38.02
(22.00)
61.04
(34.62)
55.40
(30.44)
39.60
(32.62)
37.43
(30.59)


45.01
(20.48)
46.74
(20.23)
13.44
(11.47)
41.74
(20.64)
61.89
(34.05)
59.09
(28.58)
41.75
(32.12)
39.93
(30.73)


41.20
(20.37)
42.39
(20.58)
15.88
(13.12)
37.45
(21.18)
61.91
(33.52)
55.11
(29.74)
40.15
(32.19)
37.54
(29.98)


45.76
(21.15)
47.40
(21.15)
11.93
(11.54)
42.32
(21.28)
61.01
(35.30)
59.38
(29.32)
41.19
(32.61)
39.83
(31.43)


41.68
(21.19)
43.39
(21.34)
14.16
(12.62)
38.85
(21.26)
61.70
(34.43)
55.50
(30.40)
40.93
(33.19)
37.11
(30.66)


34.44
(22.56)
35.33
(23.25)
18.33
(15.39)
30.61
(23.22)
62.28
(33.44)
47.68
(34.71)
38.39
(32.09)
32.49
(32.02)


52.54
(20.81)
54.47
(20.92)
9.47
(10.80)
49.18
(21.56)
60.62
(36.43)
66.82
(26.99)
42.94
(33.78)
44.89
(30.67)


Note: Rating scale is 0-100.









Greater pain intensity [t(46) = -4.06, p < .001, d= .93] and unpleasantness [t(46) = -3.54, p < .01,

d= .74] ratings were assigned to African-American versus Caucasian patients. Older patients

were judged to be experiencing greater pain intensity [t(46) = -4.34, p < .001, d = 1.05] and

unpleasantness [t(46) = -4.87, p < .001, d= 1.10] than younger patients. Finally, patients with

high expressivity were judged to be experiencing greater pain intensity [t(46) = -8.21, p < .001,

d= 1.83] and unpleasantness [t(46) = -7.88, p < .001, d= 2.94] than those with low pain

expressivity. Follow-up analyses were conducted to test the a priori hypothesis that gender-role

expectations about pain would influence participants' assessment ratings. Since within-cue sex

differences emerged for both pain intensity and unpleasantness ratings, follow-up analyses were

conducted separately for these decision domains. Correlation analyses indicated that the GREP

factor of "willingness to report pain" was significantly associated with average pain intensity

assessment ratings for both male (r = .31, p < .05) and female (r = .30, p < .05) virtual patients.

This factor was also significantly associated with average pain unpleasantness ratings for female

patients (r = .27, p < .05); results approached significance (r = .27, p = .052) for male patients.

The GREP factor of "stereotypic endurance" for pain was not significantly associated with

average pain intensity or unpleasantness ratings for either male or female patients; however, the

magnitude of these relationships (coefficient range: -. 18 to -.23) was sufficient as to warrant

follow-up analyses. Analysis of Covariance (ANCOVA) results indicated that the significant

within-cue sex differences persisted [pain intensity: F(1,51) = 10.73, p < .01; pain

unpleasantness: F(1,51) = 13.24, p < .01] even after controlling for the GREP factors of

"willingness to report pain" and "stereotypic endurance" for pain.

Mood Assessment

Significant differences in averaged positive mood assessment ratings emerged within age

and expression cues, with younger [t(46) = 4.44, p < .001, d= .96] and low expression [t(46) =










4.90, p < .001, d= 1.16] patients receiving higher ratings than those who were older and

displaying high pain expression. No differences in positive mood ratings were evident between

races or sexes. Turning to assessment of negative mood, nurses rated female virtual patients as

having greater negative mood than male patients [t(46) = -3.3 8, p < .01, d = .71]; older patients

as having greater negative mood than younger patients [t(46) = -4.73, p < .001, d= 1.08]; and

high expression patients as having greater negative mood than low expression patients [t(46) =

-7.96, p < .001, d= 1.69]. A trend was also observed for African-American patients to receive

higher negative mood ratings than Caucasian patients [t(46) = -1.85, p < .07, d= .42].

Treatment Decisions

There were no significant differences within cue for non-opioid treatment decisions.

Opioid treatment decisions were more likely to be endorsed for patients who were female

[t(46) = -2.75, p <.01, d= .70], African-American [t(46) = -2.39, p <.05, d= .59], older [t(46)

-3.26, p < .01, d= .77], and displaying high expressivity [t(46) = -6.25, p < .001, d= 1.38].

Follow-up analyses were conducted to test the a priori hypothesis that gender-role expectations

about pain would influence participants' treatment decisions. These analyses were confined to

ratings for opioid treatment, since sex differences only emerged for this treatment domain.

Correlation analyses indicated no significant association between the GREP factor of

"willingness to report pain" and average opioid treatment ratings for male (r = .14, p > .05) and

female (r = .15, p > .05) virtual patients. Similarly, no significant association emerged between

the GREP factor of "stereotypic endurance" for pain and average opioid treatment ratings for

male (r = .03, p > .05) and female (r = .08, p > .05) patients. Due to these non-significant

findings, no further analyses were conducted to control for the effects of gender-role

expectations about pain.









Recommendations

No significant differences emerged within race and age cues for decisions regarding

recommendations for a change in non-opioid medication. Differences were evident, however, for

sex and pain expression cues. Nurses were more likely to make change recommendations on

behalf of female [t(46) = -1.98, p = .05, d = .42] patients and those displaying high levels of pain

expression [t(46) = -2.40, p < .05, d = .46]. Examination of the average ratings for opioid-related

change recommendations indicated that such decisions were significantly more likely to be made

for virtual patients who were African-American [t(46) = -2.79, p < .01, d= .68], female

[t(46) = -2.29, p < .05, d= .52], and displaying high expressivity [t(46) = -5.57, p < .001,

d= 1.20]. Additionally, a trend was observed wherein older patients were more likely to have

such recommendations made on their behalf relative to younger patients [t(46) = -1.66, p = .10,

d= .43].

Self-reported Cue Utilization

At the conclusion of the study, participants were asked to reflect back on their experiences

during the clinical scenario portion. Specifically, they were asked what information they used

when formulating their assessment and treatment ratings for the virtual patients. Responses were

provided in an open-ended format. Several themes (not mutually-exclusive) were identified upon

inspection of these responses. Thirty-three nurses indicated that they used the facial expressions

of the virtual patients when making ratings. Vital sign information (n = 28) and patient

movement (n = 10) were the next most frequently endorsed themes. Eight nurses stated that they

incorporated text-based information about the general clinical scenario, and 7 stated that they

used text-based pain-specific information from the clinical scenario to inform their decisions.

Five nurses reported using general non-verbal information, but did not specify further. Finally, 3









nurses indicated that they relied on their clinical experience to make assessment and treatment

decisions regarding the virtual patients.

Knowledge of Study Hypotheses and Cue Utilization

Because task transparency and socially desirable responding are highly relevant issues with

analogue designs, at the conclusion of the study, participants were asked to guess at the

hypotheses of interest. Responses to this open-ended inquiry were examined for indication that a

given nurse was aware of any of the hypotheses concerning patient cues of sex, race, and age.

Targeted analyses were then conducted to determine the influence of such awareness on

assessment and treatment ratings. Due to the high volume of analyses that could be conducted,

and the consequent inflation of Type I error absent specific hypotheses, analyses were confined

to pain intensity assessment and opioid treatment ratings. These domains were chosen because

they were of most interest and relevance to clinical practice. Of the 46 nurses who responded to

the query, 11 did not give any indication that they were aware of the hypotheses of interest. The

remaining 35 gave some indication a liberal criterion was used in judging these responses of

knowledge of the pertinent hypotheses. It should be noted that few of these nurses indicated

awareness of all the study hypotheses. Results of Chi-square analyses comparing these groups in

their cue utilization indicated no differences in the directional weighting of patient demographic

(sex: X2(2) = 1.14, p > .05; race: X2(2) = .51, p > .05; age: X2(1) = 1.40, p > .05) or facial

expression (X2(1) = .48, p > .05) cues for pain intensity policies. Similarly, no group differences

were found in weightings of demographic (sex: X2(2) = .73, p > .05; race: X2(2) = .95, p > .05;

age: X2(1) = 3.08, p > .05) or facial expression (X2(1) = 1.72, p > .05) cues for opioid treatment

policies.









Exploratory Group Analyses

The following exploratory analyses were conducted to determine whether putatively

relevant participant characteristics were related to overall pain assessment and treatment decision

policies and contextual cue utilization. Given that no specific hypotheses were articulated

concerning these relationships, the results presented below should be interpreted with caution.

Participant Characteristics and Overall Decision Policies

Participants were grouped according to the significance of their overall policies. At each of

the eight assessment and treatment decisions, nurses who had a policy that was significant

(p < .05) or approached significance (p < .1) comprised one group; those with non-significant

(p > .1) policies were included in a separate group. Nurses with invariant ratings were excluded.

Table 3-6 presents the number of participants in each group. Group comparisons were then

conducted to test for differences in pertinent participant demographic and professional variables.

Table 3-6. Number of participants with significant overall policies
Policy Significant Not significant Invariant
Pain intensity assessment 39 14 1
Pain unpleasantness assessment 37 16 1
Positive mood assessment 25 25 4
Negative mood assessment 38 15 1
Non-opioid treatment 15 35 4
Opioid treatment 27 25 2
Recommendation: non-opioid 18 32 4
Recommendation: opioid 22 30 2

Participant Education and Overall Decision Policies

Chi-square analyses were employed to test for differences in nursing degree status and

overall policy significance. Results indicated no significant differences (p > .05) between nurses

who were trained at the Associate, Bachelor, and Master level in terms of their overall policy

significance across the 8 ratings.









Participant Professional Experience and Overall Decision Policies

Comparisons between participants with significant and non-significant overall policies

indicated group differences in years of professional experience for policies involving opioid

treatment [F(1,50) = 5.83, p < .05, r12 = .10] and recommendation for change in opioid

medication [F(1,50) = 4.55, p < .05, r12 = .08]. Examination of group means revealed that nurses

with a significant opioid treatment policy had fewer years of professional experience (M~= 1 1.07)

than those with a non-significant opioid policy (M~= 17.84). Similarly, nurses with a significant

policy for recommendation of a change in opioid medication (M~= 10.27) had fewer years of

professional experience than those with a non-significant policy (M~= 16.40) in this domain. No

other significant differences in professional experience emerged at the level of overall policy.

Participant Characteristics and Cue Utilization

A similar grouping scheme as that used for overall policy was employed at the cue level.

However, due to the large number of analyses required to make group comparisons across each

of the four cues at each of the eight decision policies in addition to the absence of specific

hypotheses concerning such comparisons an additional grouping method was applied. The

virtual patient cues of age, race, and sex were combined to comprise a "demographics" cue; the

pain expression cue was unchanged. Nurses were then grouped according to whether they used a

demographics cue in their various decision policies. At each decision policy, nurses who had at

least one demographic cue coefficient (age, race, sex) that was significant or approached

significance were grouped together. In the following analyses, these nurses were compared to

those who did not use patient demographic cues in their judgment policies. A similar process was

employed at the level of pain expression cue: nurses with a significant pain expression cue were

compared to those with a non-significant pain expression cue. Participants with invariant










responding were excluded from group analyses. Table 3-7 contains the distribution of nurses

across the various groups.

Participant Sex and Cue Utilization

Previous research suggests that the sex of the observer may interact with the sex of the

individual being observed to influence pain-related ratings (Robinson & Wise, 2003). Thus,

comparisons were made to determine the presence of sex differences in the utilization of virtual

patient sex cue, as well as averaged ratings of pain assessment and pharmacologic treatment.

Results of chi-square analyses indicated that male and female nurses did not differentially weight

patient sex in their decision policies about pain intensity assessment (X2(2) = .92, p > .05), pain

unpleasantness assessment (X2(2) = 3.47, p > .05), non-opioid treatment (X2(2) = .40, p > .05), or

opioid treatment (X2(2) = .67, p > .05). ANOVA results were also non-significant; in the

aggregate, male and female nurses did not provide different pain intensity [F(1,52) = 1.20,

p > .05, r12 = .02] or unpleasantness [F(1,52) = 1.67, p > .05, r12 = .03] assessment ratings, nor

non-opioid [F(1,52) = 1.52, p > .05, r12 = .03] or opioid [F(1,52) = .40, p > .05, r12 = .01]

treatment ratings for male and female virtual patients. When considered with the findings above

in the Within-Cue Comparisons section, these results suggest that although assessment and

treatment ratings differ based on the sex of the patient, these "biases" are equivalently shared by

male and female nurses.

Participant Education and Cue Utilization

Comparisons in cue utilization were made across degree status (Associate, Bachelor, and

Master) to determine if educational achievement interacted with use of contextual demographic

and pain expression information. Significant differences were found in pain cue utilization for

judgments about pain intensity (X2(2) = 6.51, p < .05). Ninety-three percent (14/15) and 82%










Table 3-7. Participant use of demographic and pain expression cues
Demographic cues
Policy Significant Not Invariant
significant
Pain intensity 27 26 1
assessment
Pain unpleasantness 29 24 1
assessment
Positive mood 25 24 5
assessment
Negative mood 26 27 1
assessment
Non-opioid 15 35 4
treatment
Opioid treatment 27 24 3
Recommendation: 16 34 4
o\non-opioid
Recommendation: 20 32 2
opioid
Note: Values represent number of participants in each category.


Pain expression cue
Significant Not Invariant
significant
41 12 1










(18/22) of nurses with a Master' s and Associate' s Degree, respectively, had a significant

regression coefficient for pain expression, whereas only 56% (9/16) of those with a Bachelor' s

Degree significantly weighted pain expression in their policies for pain intensity assessment.

Similar results were obtained for pain unpleasantness assessment (X2(2) = 5.82, p = .055), such

that 93% of Master' s-trained, 77% of Associate' s-trained, and 56% of Bachelor' s-trained nurses

had a significant pain unpleasantness policy in which the pain expression cue played a prominent

role. All other degree-based comparisons regarding utilization of demographic and pain

expression cues were non-significant (p > .05).

Participant Professional Experience and Cue Utilization

Results of ANOVAs comparing nurses who did and did not have significant demographic

cue coefficients indicated no differences in years of professional experience between these two

groups. When similar comparisons were made regarding pain expression cue utilization, two

significant differences emerged. In the context of opioid treatment policies, nurses with a

significant pain expression cue (M~= 10.54 years) had less professional experience

[F(1,49) = 7.93, p < .01, r12 = 14] than nurses with a non-significant pain expression cue

(M~= 18.30 years). A similar result was found for opioid-related recommendation policies, such

that participants who used pain expression as a significant cue (M~= 10.76) had less professional

experience [F(1,50) = 4.24, p < .05, r12 = .08] than those who did not (M~= 16.63). No significant

differences in professional experience emerged for the other decision policies.









CHAPTER 4
DISCUSSION

Issues related to the assessment and treatment of pain have received increased theoretical

and empirical attention over the past several years. Taken in its entirety, this body of work

indicates that clinical pain is frequently inadequately assessed and under-treated (e.g., Cousins,

1994; Manyande, 1996; Thomas et al., 1998; WHO, 1986). Patient characteristics in particular,

sex, race/ethnicity, and age have been identified as a potential source of these deficiencies

(e.g., Anderson et al., 2000; Cleeland et al., 1994; Horgas & Elliott, 2004; McDonald, 1994; Ng

et al., 1996b; Oberle et al., 1990; Robinson & Wise, 2003). Unfortunately, methodological

limitations of previous investigations in this field place considerable constraints on the

conclusions that may be drawn from them. The current study sought to address several of these

limitations through implementation of an innovative research design and methodology.

Additionally, this investigation was structured to provide a more detailed analysis of the clinical

decision making process in order to better characterize the extent to which patient characteristics

influence provider decision-making about pain. Overall, results indicated that the virtual patient

technology and lens model methodology were successful in capturing and detailing the pain-

related decision policies of nurse participants. Although replication of this success is certainly

needed, the current investigation illustrates an alternative and promising approach by which to

continue the study of medical decision-making.

Results of idiographic analyses of pain assessment ratings indicated that approximately

70% of nurses had significant decision policies in this domain. Stated differently, the contextual

information provided in the clinical scenarios was sufficiently weighted by the maj ority of nurses

to result in a reliable decision product. It would appear, then, that despite the constraints on cue

number that lens model designs impose (see discussion below), highly relevant information was










provided for clinical decisions about pain assessment. Although negative mood assessments were

similarly reliable, ratings of positive mood were less consistent. This is to be expected given the

clinical context. Assessment of negative mood in a patient apparently even a virtual one -

experiencing acute, post-operative pain should be less subj ect to error than assessment of

positive mood in that same patient, given that negative mood is more consistent with the

experience of pain (Robinson & Riley, 1998) and likely to be more frequently encountered by

healthcare professionals.

Analyses of pain treatment policies indicated that almost twice as many nurses had

significant opioid than non-opioid policies. For these particular healthcare providers, the

information contained in the clinical scenarios was apparently more serviceable for decisions

about the use of opioid medications than non-opioid medications. It is also possible that these

results are due to nurses' greater familiarity with opioid medications in an acute pain context

and/or the relative paucity of guidelines specific to non-opioid medications. In the final decision

domain recommendation for a change in medication a significant non-opioid

recommendation policy was found in one-third of nurses, whereas 41% had a significant overall

opioid policy. These data suggest that when compared with other decision domains, particularly

the assessment policies, decisions about medication recommendations were less influenced by

the contextual cues available to study participants. This is not surprising when one considers that

the other decision domains were likely to be perceived as more precise relative to this domain

(see below). If so, the decision policies of individual nurses would be expected to be less

consistent, resulting in fewer significant overall policies. There is also likely to be considerable

differences among nurses in terms of their comfort in making medication-related

recommendations. These differences may be a product of the considerable variability in the









medical cultures within which individual nurses practice (Casanova et al., 2007; Irvine et al.,

2000; Pollard, 2003).

The consistency of assessment policies pain and mood relative to both medication and

recommendation treatment domains also suggests that fewer additional cues are needed by

nurses when making decisions about the experience of pain and mood in their patients. This is

supported by the finding that the greatest number of available cues were used for pain and mood

assessment policies. In contrast, the contextual cues contained in the clinical scenarios were not

sufficient, and may not have even been necessary, to produce a reliable decision product in a

large number of nurses in this study, particularly in the non-opioid treatment and both

recommendation domains. Future research is needed to further identify those cues that are

particularly germane to these treatment decisions.

A maj or innovation of the current methodology lies in its ability to capture the decision-

making process as well. Analyses at this level indicated that patient demographic cues played a

significant role in many nurses' assessments of pain intensity and unpleasantness. The vast

maj ority of those who used sex as a significant cue tended to assign higher pain ratings to female

patients. In addition to its statistical significance, this cue accounted for a rather substantial

amount (up to 14%) of the total variance in the pain assessment decision policies of some nurses.

When ratings were averaged across participants for more traditional nomothetic analyses, female

videos received significantly higher ratings than male videos, even after controlling for

stereotypic beliefs about gender and pain.

These results are interesting when considered in light of the literature that indicates

females are at greater risk of having their pain under-assessed in the clinical context (Anderson

et al., 2000; Cleeland et al., 1994), whereas in the experimental setting, females are judged to be










experiencing greater pain than males (Robinson & Wise, 2003). The current study differs in

many respects from these previous investigations. An atypical and innovative hybrid design was

employed in that participants made assessment ratings of a clinical nature in an experimental

context. Differences in study participants are also noteworthy. This study was largely comprised

of female nurses, whereas the aforementioned clinical investigations consisted mainly of male

physicians; participants in the Robinson and Wise study were primarily college undergraduates

with a roughly equal proportion of males and females. In the current study, no differences were

found between male and female nurses' utilization of patient sex cue, or in their averaged ratings

for male and female videos. Although lack of statistical power should be considered given the

small proportion of male nurses, the corresponding effect sizes were small, suggesting that any

differences that do exist are likely to be of little consequence. These across-study differences

make a clean synthesis of this literature difficult. Although the current results are supportive of

the hypothesis that, relative to males, females are viewed by others as having greater pain, future

investigations are needed to further elucidate the role of patient and provider sex in decision-

making about pain assessment. Greater variability in provider sex and professional role are

particularly critical.

The virtual patient race cue also emerged as a prominent contributor to many nurses' pain

assessment policies. Most nurses who significantly weighted patient race assigned higher pain

ratings to African-American patients, and the magnitude of this cue's effect was similar to that of

patient sex. At the nomothetic level, African-American patients received significantly higher

ratings of pain intensity and unpleasantness than their Caucasian counterparts. These Eindings

were surprising and counter to a priori hypotheses. The relevant literature on this topic is small

and mixed, with some evidence that African-American patients are at greater risk of having their










pain underestimated relative to Caucasians (Cleeland et al., 1997) and some reports of no

racial/ethnic differences in pain assessment (Todd et al., 2000). To the author's knowledge, this

is the first study to find that African-American patients received higher pain ratings than

Caucasians. The same unique characteristics of the current study noted above also hold relevance

here in terms of understanding these disparate findings. Methodological differences between the

studies that have been conducted on this topic are significant and, when taken together with their

small overall number, place considerable constraints on the drawing of overarching conclusions.

It is, at present, unclear why patients of different racial/ethnic backgrounds were judged to

be experiencing different levels of pain in the current study despite the fact that contextual

information particularly pain expression was standardized across patient. Perhaps study

participants were sensitive to scientific and media reports of racial/ethnic disparities in medical

care and took particular caution not to underestimate relative to Caucasian patients the pain

experience of African-American virtual patients. Perhaps female nurses are less likely to hold

and/or act on biases concerning minority populations. The development of multicultural

competence is a recurrent theme in the nursing literature and highly emphasized among nursing

education programs (Fitzpatrick, 2007; Hughes, & Hood, 2007; Lipson, & DeSantis, 2007;

Robinson, 2000; Underwood, 2006). It is certainly possible that the effects of such attention in

the nursing field were transferred to pain assessment ratings in the current study. It is also

difficult to ascertain the meaning of these racial/ethnic differences in pain assessment ratings. In

judging African-American virtual patients as having greater pain relative to Caucasian patients,

did nurses discount the pain experience of Caucasian patients? Did they view African-Americans

as being less able to tolerate similar pain experiences? These are intriguing questions that could

not be addressed by the current study. Regardless, that significant differences in pain assessment










at both the idiographic and nomothetic levels emerged is cause for concern and continued

research.

The Einal patient demographic cue age also played a significant role in the pain

assessment of many nurses. In fact, almost one-quarter of nurses weighted age in their

assessment decisions, with all but one of these providing higher pain ratings for elderly patients.

The greatest amount of variance in these decision policies accounted for by age (over 25%) even

exceeded that of patient sex and race. Averaged ratings across study participants also showed

that older videos received significantly higher ratings than younger videos. The literature

documenting the under-assessment of pain in older individuals is robust (Cohen-Mansfield &

Lipson, 2002a, 2002b; Cook et al., 1999; Ferrell, 1995; Ferrell, 1996; Gloth, 2000; Horgas &

Elliott, 2004; Kaasalainen et al., 1998; Sengstaken & King, 1993); thus the current results were

not expected. However, much of this literature has focused on the comorbid medical conditions -

particularly dementia that are more prevalent in older populations and likely to complicate the

assessment of pain in these patients. The current clinical scenario was standardized, such that

older and younger patients presented with equivalent conditions. That older patients were judged

to be experiencing greater pain than younger patients is, therefore, particularly intriguing. As

discussed above in the Eindings regarding race, the precise implications of these results are not

clear. Was the pain experience of older patients over-estimated? Or was the pain experience of

younger patients under-estimated? Without patient self-report information which, for obvious

reasons, was not possible in this study these questions remain unanswered. They do, however,

provide direction for future research efforts in this domain. The inclusion of relevant comorbid

conditions as an additional cue in future investigations would also likely prove fruitful in further

elucidating the role of patient age in the assessment of pain.










The pain expression cue emerged as a highly important contributor to assessment ratings;

approximately 70% of study participants reliably used this cue when making decisions about

pain intensity and unpleasantness. All of these nurses judged patients displaying high levels of

pain expression to be experiencing greater levels of pain. Further, up to 79% of the variance in

these ratings were accounted for by patient expression. Consistent with these findings, results of

nomothetic analyses indicated large differences in average pain intensity and unpleasantness

ratings of high vs. low pain expression videos. The methodological implications of these results

are encouraging. Although the facial manipulations of virtual patients were guided by the FACS

and, thus, were expected to closely approximate the empirically-validated pain expression (Craig

et al., 1992; Prkachin, 1992b), the current findings regarding participant use of this cue provide

further validation of the manipulation. As a clinical matter, it is reassuring that pain expression

was the most salient cue in pain assessment policies. This finding is consistent with previous

work highlighting the considerable influence of nonverbal expressions on observers' ratings of

pain in others (Ahles et al., 1990; Hale & Hadjistavropoulos, 1997). In fact, observers have been

found to rely more on such nonverbal expressions than even self-report (Craig, 1992; Poole &

Craig, 1992). Replication of these findings with virtual patient methodology would be an

interesting future endeavor.

Turning to policies regarding the treatment of pain, when these ratings were submitted to

idiographic analyses virtual patient sex played a relatively minor role in decisions about non-

opioid treatment, with only two nurses significantly weighting this cue. Opioid-related decisions,

on the other hand, were more frequently influenced by patient sex. With one exception, when

patient sex served as a significant cue, female patients received higher ratings and, thus, were

more likely to be administered medication for pain. Although fewer nurses reliably used this cue









for treatment decisions, the policies of those that did were even more affected by sex than in the

assessment domain. At the nomothetic level, sex differences were found only for opioid

decisions, with females, again, more likely to receive such treatment.

That patient sex had an influence on the pain-related treatment decisions for many nurses

was not surprising. It was surprising, however, and counter to study hypotheses, that when sex

was a significant cue female patients were more likely to receive pain medication than males.

There is substantial evidence that females receive sub-optimal pharmacologic management of

their pain relative to males (Beyer et al., 1983; Calderone, 1990; Cleeland et al., 1994; Cohen,

1980; Faherty & Grier, 1984; McDonald, 1994; McDonald & Bridge, 1991). It is important to

note, however, that sex differences are not always found (Bartfield et al., 1997; Turk & Okifuji,

1997, 1999). Differences have even been found in the reverse direction, with early studies

indicating that females were the recipients of more aggressive treatment (Bond & Pilowsky,

1966; Pilowsky & Bond, 1969). The current results are consistent with this early literature. Bond

(1971) interpreted his findings in the context of a culturally-sanctioned belief system wherein

males are expected to be more tolerant of pain than females. Although intuitive, this conclusion

was speculative absent any additional, supportive data. The inclusion of a measure of gender-role

stereotypic beliefs about pain in the current study provided an opportunity to directly test this

hypothesis. Results indicated no significant relationship between these beliefs and treatment

ratings. Thus, it would appear that, as measured by the GREP, providers' pain-related stereotypic

beliefs do not explain the sex difference in their treatment of pain. What, then, is driving these

results? Potential explanations lie in the sex and role of the provider. As noted above, previous

investigations have largely included male physicians. The fact that the current study enrolled

only nurses, most of whom were female, could explain the disparate Eindings. Although the









current study attempted to address the provider sex issue through targeted recruitment, this effort

was likely not successful in securing enough male nurses for adequately powered sex-based

comparisons in treatment policies. In regards to provider role, it is also possible that treatment-

related sex differences are more likely to manifest in the context of medication prescription and

administration primarily a physician activity and are less prominent in clinical activity that

consists solely of medication administration, an activity that is largely the purview of nurses.

This hypothesis is speculative but, if true, could explain the discrepancy between the current

results and those of other recent investigations.

Patient race was significantly weighted by 6% and 9% of nurses, respectively, in their non-

opioid and opioid treatment decisions. With one exception in each domain, nurses were more

likely to engage in these treatment practices with African-American patients than Caucasian

patients. Despite their small number, the nurses who significantly weighted race in their policies

did so to a larger degree than in the assessment domain. In the aggregate, a medium-sized

difference was found, such that African-American patients received significantly higher opioid

treatment ratings than Caucasians. No race differences were found in averaged non-opioid

ratings. As in the pain assessment domain, these results are in conflict with the relatively large

and consistent literature demonstrating that minority individuals' pain is under-treated relative to

individuals of the dominant racial/ethnic background (Anderson et al., 2000; Bernabei et al.,

1998; Cleeland et al., 1994; Cleeland et al., 1997; Ng et al., 1996a; Ng et al., 1996b; Ross, 2000;

Sambamoorthi et al., 2000; Tamayo-Sarver et al., 2003b; Todd et al., 1993, 2000). Several

explanations have been articulated to account for these findings. In addition to frank racism on

the part of providers, a widely held explanation is that patient race serves as a proxy for the true

operating variables that drive these treatment differences. These include factors such as









differential communication, SES, and access to healthcare. Aside from the cues of interest that

were systematically manipulated across virtual patients, all other factors in the current clinical

scenarios were held constant. The findings that African-American patients did not receive sub-

optimal treatment compared to Caucasian patients and, in fact, were the recipients of more

aggressive pharmacologic care in many instances are more in line with a "race-as-proxy"

explanation than one based on provider racism. Future investigations that include cues such as

patient SES are needed to better address these important issues.

The speculative hypotheses articulated above concerning provider sex and role may also be

extended here. In fact, there is evidence that females harbor less racial/ethnic biases than males

(Bier, 1990; Johnson, & Marini, 1998; Qualls, Cox, & Schehr, 1992; Schuman, Steeh, & Bobo,

1997; Wuensch, Campbell, Kesler, & Moore, 2002). The findings observed in the present study

could, thus, be attributable in part to the overwhelming maj ority of female participants. It will be

interesting to revisit this issue in the future when a sufficient number of male providers have

been enrolled. An additional factor that may bear on the differences between the current results

and those of previous studies concerns the manner in which treatment decisions were

operationalized. The current study examined nurses' likelihood of administering a given

medication, whereas previous studies have largely focused on the amount of medication

administered. This seemingly subtle difference could, in fact, be quite important. Future studies

would need to include both likelihood and amount ratings in regards to medication-related

decisions to further clarify this issue.

The context in which the pain is occurring may also have implications for these

differences. The post-surgical scenario employed in the present investigation is rather

straightforward and, thus, may be less likely to elicit non-medical influences (e.g., racial biases)










on providers' treatment-related decisions than more ambiguous scenarios such as migraine or

sickle cell crises (Tamayo-Sarver et al., 2003b). It is also possible that the type of methodology

used to investigate these issues is of importance. Despite its novel innovations, the current study

is still properly classified as a vignette-based approach. Two other recent vignette studies found

that race of hypothetical patients did not influence analgesic practice among medical providers

(Campbell, 2002; Tamayo-Sarver et al., 2003a). Perhaps these designs provide sufficient shelter

from the time and financial pressures of real medical practice that may make biased decisions

more likely. To address this hypothesis, future modifications to the current approach could

include time constraints, such that assessment and treatment decisions must be made within a

circumscribed period of time.

When patient age was analyzed at the individual level, it emerged as a significant cue in

the non-opioid treatment policies of 1 1% of the study sample. Seventeen percent had opioid

policies in which age played a significant role. The magnitude of these effects was roughly

equivalent to pain assessment policies. The direction of the age effect was somewhat mixed for

non-opioid decisions, the effect of which was seen when ratings were aggregated and no

significant differences emerged between younger and older patients. More consistent results

were found for opioid treatment; the maj ority of nurses were more likely to administer opioid

medication to older patients relative to younger ones. This greater consistency was borne out at

the group level, as a moderate-to-large age difference was found.

The relevant empirical literature is small and, thus, should be approached with caution.

Nevertheless, in contrast to the current data, Oberle and colleagues (1990) found that post-

surgical elderly patients received less medication for pain than their younger counterparts. In a

vignette-based study, Campbell (2002) also found that hypothetical elderly patients were at










greater risk of being under-medicated for acute pain relative to younger ones. Returning to the

current results, not only is it surprising that elderly patients often received higher treatment

ratings, that this was particularly so at least in terms of the directional consistency of the effect

- for opioid medication is remarkable. It is well-documented that providers are increasingly

reluctant to administer this class of medications, even in the most severe cases (Portenoy, 1996).

Thus, it would be expected that age-related practice differences elderly receiving more

conservative treatment as assessed in the current study would be more likely to manifest for

decisions about opioids. Since, relative to their younger counterparts, older individuals are more

likely to present with a larger array of comorbid medical conditions (Anderson, & Horvath,

2002; Hoffman, Rice, & Sung, 1996; Wolff, Starfield, & Anderson, 2002), the current findings

may be a consequence of the standardization of clinical presentation across virtual patients.

These results tentatively suggest that when older and younger patients present with similar

conditions both number and type older patients, by and large, receive equivalent pain-related

pharmacologic treatment and may even be the recipients of more aggressive treatment by some

nurses.

The level of pain expression displayed by virtual patients significantly influenced

treatment decisions regarding non-opioid and opioid medications in 24% and 46% of study

participants, respectively. Interestingly, the direction of this effect was approximately equally

split for non-opioid decisions and entirely consistent for opioid decisions, with high expression

patients receiving higher ratings. As with pain assessment ratings, this cue had the largest

absolute effect on nurses' overall policies in the treatment domain. When analyzed

nomothetically, not surprisingly given the roughly equal split, no difference emerged in average

non-opioid ratings between patients displaying low and high pain expressions. In contrast, a










large difference was found for opioid treatment ratings; high expression patients were more

likely to be administered medication than low expression patients. The natural question that

arises from these sets of findings is, relative to their directional counterparts, did those nurses

who were less likely to administer non-opioid medication to high expression patients provide

higher opioid medication ratings for these patients. Follow-up analyses indicated that this was

not the case; there were no differences in opioid ratings for high or low expression patients -

between the two groups who used the pain expression cue in opposite directions for non-opioid

treatment decisions. What then accounts for these individual differences in cue use?

Unfortunately, that question must remain unanswered at present, but is an important

consideration for future investigations.

The utilization of patient demographic cues in decision-making about pain assessment and

treatment, even if found in only a small cluster of nurses, is highly significant when one

considers several features of current nursing practice. The first concerns nurse-to-patient ratios.

California is currently the only state to set a ratio limit for inpatient hospital units (Buchan,

2005). Medical/surgical units the most relevant to the current discussion are prohibited from

exceeding a ratio of 1:5. If one adopts this as a conservative estimate of the average nurse-to-

patient ratio across the country, then a given full-time nurse has the opportunity to assess and

treat the pain of hundreds of patients each year and thousands of patients in a career. In this

context, even a small propensity to utilize patient demographic characteristics in pain-related

decision-making is of considerable importance. A second relevant feature of current nursing

practice is related to training and modeling. The precepting of new nurses is a very important

aspect of professional nursing practice (Alspach, 2000; Hardy, & Smith, 2001; Shamian, &

Inhaber, 1985; Speers, Strzyzewski, & Ziolkowski, 2004). If, in this capacity, a veteran nurse









conveys the message the self-reported cue utilization data from the current study suggest that

this is likely to be an implicit process that patient demographic characteristics are to be

considered when making pain-related decisions, the consequences could be dramatic. Not only

would the precepted nurses be more likely to engage in similar practices, but if they served as

preceptors in the future, the transmission of such inappropriate clinical behavior is furthered still.

Similar dissemination is likely to also occur outside of formal training in the form of modeling.

Thus, any one nurse with a given propensity to weight patient demographic cues in the

assessment and/or treatment of pain could have a far-reaching influence on clinical practice.

Policies regarding the likelihood of recommending a change in medication to obtain better

pain control were also influenced to some degree by contextual cues. Patient sex played a

relatively minor role in the decision-making process for most nurses; however, for the few that

did use this cue in a reliable manner, the effect was quite substantial. When ratings were

combined for group analyses, female patients received significantly higher ratings for both

medication classes. Race was a somewhat larger factor in this context. At the idiographic level,

the direction of effect was approximately equal for non-opioid medications, which manifested in

a non-significant within-cue difference in aggregated ratings for African-American and

Caucasian patients. In contrast, the results were entirely consistent for opioid recommendations.

All 4 nurses with a significant race cue made higher likelihood ratings on behalf of African-

American patients. Although these nurses represented less than 10% of the entire sample, their

use of virtual patient race was enough to produce a significant and relatively substantial

aggregate difference between ratings for African-Americans and Caucasians. Virtual patient age

had a minor impact on non-opioid recommendation decisions but a somewhat larger one for

opioid decisions. More nurses gave higher non-opioid recommendation ratings to younger










patients than older ones; however, the reverse was true for opioid medication. When these data

were collapsed for group analyses, no age differences emerged for either medication. Finally,

and consistent with previous results, virtual patient pain expression played a relatively large and

consistent role in recommendation decisions. Most nurses who used this cue in a consistent and

significant manner were more likely to make recommendations on behalf of patients displaying a

high pain face. It is not surprising, then, that similar significant within-cue differences for pain

expression emerged at the nomothetic level.

A precise elucidation of the above results is difficult at this time. Perhaps the most pressing

interpretive challenge is that the intent of the ratings is not known. It is likely that while some

nurses made ratings with the obj ective of recommending a change in medication tyipe, others

were pursuing a change in medication dosage; still others could have intended something

different altogether. Without this level of informational detail, further interpretation of these data

is exceedingly speculative and, thus, imprudent. Future studies could address these issues by

having nurses indicate their intent and likelihood.

Although not a primary target of the current investigation, the impact of contextual cues on

mood assessment ratings was also determined. Virtual patient sex emerged as a relatively

insignificant cue in the assessment of positive mood but a more influential constituent of policies

involving negative mood assessment. When virtual patient sex cue was weighted by participants,

males tended to be viewed as experiencing a more favorable mood (less negative, more positive),

whereas the opposite (more negative, less positive) was true for females. Research supports the

existence of strong, sex-specific socialization pressures that bear on the judgment of another' s

emotional experience. For example, Gaelick, Bodenhausen, and Wyer (1985) found that

expressions of happiness and anger by women were perceived as more and less intense,










respectively, than expressions of equivalent intensity displayed by men. Even young children are

affected by these stereotypes (Haugh, Hoffman, & Cowan, 1980). An interesting reversal of the

stereotype was reported by Hess, Adams, and Kleck (2004), in that women were rated as more

angry and men as more happy. The current results are consistent with this study by Hess and

colleagues, and although both of these findings could be spurious merely the result of chance -

they can also be meaningfully interpreted in the context of the Shifting Standards Model

(Biernat, Manis, & Nelson 1991). Briefly, this model posits that subj ective judgments of

individuals from different social groups may fail to elicit the stereotyped expectations of judges,

because they invite the use of different evaluative standards. As applied to the current study,

males and females are generally expected to display more negatively and positively valenced

emotions, respectively. Consequently, a given negative emotional expression an expression of

greater pain in the current study is perceived as more negative when displayed by a female than

a male. Conversely, a given positive emotion is rated as more positive when expressed by a male

than a female. This interpretation is tentative but provides an intriguing basis for future

investigations of the pain-mood nexus.

As with virtual patient sex, there was directional variability in individual nurses' use of the

race cue. Nevertheless, more nurses were inclined to view African-Americans as experiencing

less favorable mood than Caucasians. Cross-cultural recognition and categorization of emotional

expressions has received considerable empirical attention (Dickey & Knower, 1941; Ekman &

Friesen, 1972; Fridlund, Ekman, & Oster, 1987; Izard, 1968; Matsumoto, 1987; Schimmack,

1996; Shioiri et al., 1999). However, this research has been overwhelmingly asymmetrical in its

focus on the race/ethnicity of the observer to the conspicuous exclusion of the race/ethnicity of

the observed individual. Given the lack of variability in the racial/ethnic background of










participants, the current study focused exclusively on the characteristics of the observed patient.

Consequently, the voluminous literature on cross-cultural issues in the expression and perception

of emotional states provides little interpretative guidance for the current results.

Of all the demographic variables, age demonstrated the largest and most consistent impact

on mood assessments, with approximately one-quarter of participants using this cue in a

significant manner. With one exception, these nurses rated younger patients as experiencing

more favorable mood than their older counterparts; the directional consistency of this effect was

borne out in group analyses of average ratings. A small literature exists on the perception of

elderly individuals' emotional expressions. In contrast to the current Eindings, Borod and

colleagues (2004) found no difference in intensity ratings of positive and negative emotions

expressed by individuals of varying age groups. A similar Einding was reported by Moreno,

Borod, Welkowitz, & Alpert (1993) in which the age of the expressor did not influence the

perceptual accuracy scores of raters. In contrast, Malatesta, Fiore, & Messina (1987) found that

the emotional expressions of older subj ects were not perceived with clarity. Although this study

lacked an adequate, younger comparison group, it does suggest that older persons are at risk for

misattribution of their emotional displays. Given the substantial differences in methodology and

purpose, the relevance of this literature to the current study is unclear. Additional research is

needed to clarify the effect of patient age and other demographic characteristics for that matter

- on providers' emotional assessment in a clinical pain context, a topic that has not received

adequate attention to date.

The Einal cue of interest pain expression again emerged as a strong and consistent

contributor to decision policies in the domain of mood. Forty-four percent and 63% of the

sample manifested a significant pain expression policy in assessments of positive and negative










mood, respectively. All rated highly expressive patients as experiencing less favorable mood.

These results are not surprising considering the overlap in core action units namely AUs 4 and

7, which correspond to movements of the brow and eyes, respectively of various negative

emotional states (e.g., anger, fear, sadness) and pain. From a conceptual point of view,

concomitant negative affect is expected with the experience of pain. Indeed, the IASP (1994)

defines pain as "an unpleasant sensory and emotional experience associated with actual or

potential tissue damage or described in terms of such damage" (italics added). Thus, even in the

absence of "explicit" mood-related information, one would expect the assessment of mood -

particularly negative mood in a clinical pain context to be a relatively reliable process and

amenable to statistical modeling.

An interesting issue that emerges from the discussions above concerns the integration of

idiographic and nomothetic results. The vast maj ority of scientific investigations involving

human participants are nomothetic; i.e., data is collected from individuals and aggregated for

group analyses. The current study highlights a maj or limitation of a strictly nomothetic approach,

namely, that it provides an incomplete perspective on the phenomenon of interest. To take an

example from the current data, if one limited the analysis of patient race and opioid treatment

decisions to solely a nomothetic approach, the conclusion would consist only of the following:

African-American patients received significantly higher opioid treatment ratings than

Caucasians, and this difference was of a medium-size effect. If, however, an idiographic

perspective was added, the conclusion would be much different. Results of an idiographic

analytic approach indicated that, far from being a ubiquitous phenomenon, such race-based

differences in opioid treatment were only present in 9% of study participants. And of these 9%,

one nurse' s use of the race cue even operated in the opposite direction. The implications of these









results are very different. A strictly nomothetic approach could possibly lead to calls for

sweeping intervention efforts aimed at addressing this "problem" of differential pain treatment

practices among racial groups. On the other hand, idiographic results suggest a more nuanced

approach, one that involves the identification and targeting of those individuals who produced

discrepant ratings across race. The current study was primarily focused on the contextual cues of

interest and, consequently, did not secure sufficient variability in key provider characteristics to

address the individual differences question. Nevertheless, results were suggestive of the

influence of provider education and experience on pain-related decisions. Continued systematic

efforts to include providers of diverse personal and professional characteristics are needed to

further this next step in the literature, namely, the identification of which providers are most

likely to weight patient demographic cues in their clinical decision-making about pain. The

idiographic-nomothetic distinction highlighted in the current study also provides an alternative

lens through which to view the existing literature in this field, as well as a unique methodological

approach by which to continue this line of research.

A significant limitation of lens model designs is the necessity to restrict the number of

contextual cues available to study participants. The purpose of this restriction is primarily two-

fold. First, the cognitive capacity of human participants is limited; only 7 + 2 independent pieces

of information (i.e., cues) can be processed at any one time (Miller, 1956). Thus, Cooksey (1996)

recommends that no greater than nine cues be included in a study. Inclusion of a greater number

of cues increases the probability of inconsistent responding, inconsistent cue use, and/or ignoring

of cues. A second reason for limiting the number of available cues has to do with the number of

scenarios presented. As the number of cues increases, the number of scenarios needed also

increases, but at an exponential rate. The mere addition of a few more cues in a given study










could easily increase the necessary number of clinical scenarios to a practically infeasible total.

The consequence of such cue restriction is that many potentially important cues to the decision

process of interest must be excluded from any one study. One way of determining the relevance

of cues included in a particular study, and of the need in future work to incorporate additional

cues, is to examine the coefficient of determination (R2) for a given policy. In the current study,

when averaged R2 ValUeS were compared across decision domains, pain assessment policies

emerged at the top. Further, the greatest proportion of study participants had a significant overall

policy in the assessment domain. These results indicate that the available contextual cues were

highly relevant to decisions about pain assessment. Conversely, additional cues are apparently

needed to better capture the decision making process in the domains of non-opioid treatment and

medication-related recommendations.

A shared feature of all empirical investigations is the presence of limitations. Although

several limitations of the current study have been noted throughout the discussion, perhaps the

most conspicuous limitations are related to its analogue nature. All analogue studies must

strongly consider their representativeness of the topic of interest as it actually exists in the world.

The current study attempted to mitigate this limitation, while still exerting rigorous experimental

control, through the use of virtual patient technology. This innovation notwithstanding, study

participants were still making clinical decisions about a hypothetical virtual patient in a contrived

experimental setting delivered via computer. Furthermore, the videos captured only the head and

face of virtual patients and contained no verbal component. To address this issue of

representativeness, feedback was elicited from nurse participants concerning several features of

the study. Over 70% indicated that the virtual patient facial expressions were realistic depictions

of pain. Over 90% considered the clinical scenario to be reflective of a real post-operative









scenario. Over 80% of participants rated the patient information as similar to that encountered in

a real clinical setting. Finally, over 70% stated that their decisions regarding the treatment of the

virtual patients were similar to decisions they would make regarding a real patient. Although

these responses suggest a high degree of representativeness, one must remain cautious when

attempting to extend these findings to actual clinical practice. A second issue concerns the face

validity of the investigation and consequent potential for participants to respond in a socially

desirable manner. Although many nurses in the current study expressed at least partial awareness

of the hypotheses of interest, they did not differentially weight patient demographic or facial

expression cues relative to those who did not express such awareness. This lack of a difference

may be due, in part, to the fact that all nurses in this study were unaware of their use of such

cues. In fact, not one nurse indicated that s/he used a patient demographic cue for any of the

decision domains.

In conclusion, the current study found that the patient demographic cues of sex, race, and

age are significant contributors to the pain-related decision policies of many nurses. The level of

pain expressed by the virtual patient was also found to be an important factor in this context. In

addition, this study demonstrated the use of innovative novel virtual patient technology and lens

model methodology in the investigation of these highly important issues. Continued research is

needed to address the many questions raised herein, with the goal of improving the assessment

and management of all patients suffering from pain.










Tabe A1. olRESUL TS OF IDIOGRM HIC DXGRES SION ANAL YSES




52 .253 -016* .1303*
6 ~.491** .104 .097.73* 72*
7 -.084 .118 .630** .658**
.57* 4 .196 .174 .089
98 .2078 .155 .2243* .552** 64*
8 2.173 .381* .545** .444**
11 .070 .249 -00422 .485** .412**
-.126 .005 .8 436* .264T
2-.0265 .057 .170 .603** .408**
13 -.098 .153 .66 .792** .684**~
154.498 .200 31553 .094 .065
16 -.005 .099 -049 .472** .4609**
17 20288 .00599 294 075490* .506 *
198 .121 -.178 g9 .3550* .31**
22 01 0 33 23 6 03 6365 86 7 *
2 2 200666 .00774 2 4 9 97
223 .24* .061 -.060 .652** .477**
4 -.158 -.058 -.157 89* .5*
65 .44 -201 0 00.605** .397**
27 .239 .274 .104 .489* .1435*
28 .148 .167* -164- -.007 .8143
29 .318t .169 -1 89* .1*
.32* 0582 .186 .165
30 .79* 405 .315* .586** .600**
332.027 -.102 .000 .420* .228
333 -024 306538 058 606207 40856

345 -.79 -.8* .005 .035 .134
365 .211 .1805 00400 .700** .576**
37 022890* 0 70 .249 33 03
38 .261 .289 .5 .670** .528**
09 .080 -.080 -02649 .476** .425*










Table A-1. Continued
Participant Age Race Sex Pain R2
41 -. 169t -.003 .090 .877** .805**
42 .256 .054 .112 .425* .262t
43 .309t .288t .116 -.133 .209
44 .172 .136 .335** .733** .697**
45 .044 .086 -.065 .467** .232
46 .051 -.093 -.157 .469* .262t
47 .296* -.070 .206t .706** .633**
48 .153 .173 .009 .792** .680**
49 .369** .252t .098 .580** .547**
50 .226 .011 -. 138 .414* .242t
5 1-- - - - - - - -- - -
52 .041 .209 -. 116 -.058 .062
53 .107 .000 .064 .735** .556**
54 -.056 .153 .237t .704** .578**
**p<.01,*p<.05, tp<.10








Table -2 Policistwr


43 .204 .016 -1364 .378 .248'
.181 .186 .7 .031 .184
5 .10** 1786 -081 .802** .718**
6 .5085 .0178 .216* .649** .760**
7 .052* 1451 .195 .159 .074
8 .829* .2459 .266* .523** .645**e
S.2182 .23*' .1 472** .368*
.050 .192 .024 .552** .494**
12 .032 -.104 .304* .537** .328*"
13 .119 131 .50. 781** 441**
13 .166 .103 .190 .778* .672*

76 -.014 .072 3058 .886** .832**Y
18 .121 -.093 -11.101 .145

20 .043 3403 .161 .571** .388**
21 -.053 2330 -.017 .157 .143
22 .2093 064 348* .548** .478**
23 .243* 030 -.055 .643** .465**
2254 .111 012 .00096 88* .8*
226 024 28 8329 4" .485
27 .21*8 1206 -0.034 .107
28 .294* 2156 .191* .831** .779**
28 004 75 1 15056 16
-.4129 .354 -30988 05638* .459**c
31 .152 -.1324*2 -. 51 .145
72 .089 0691 40 .659* .155*
3 .1234 219' 00 .578* 368*

39 .286 -059 234 .537** .446**~
41 .136 .321* 0137 98 .143
42 .191 .000 .2 872** 659**Y
43 .203 .046 .92 .887** .838**
.07 .11 .3 029707










Table A-2. Continued
Participant Age
44 .143
45 .050
46 .390*
47 .292*
48 .206?
49 .387**
50 .130
51 - -
52 .177
53 .177
54 -.108
**p<.01,*p<.05, tp<.10


Race
.113
.145
.034
-.085
.201
.244?
-.152

.157
.032
.134


Sex
.303**
-.061
-.074
.2047
-.001
.120
.152

-.218
.121
.186


Pain
.767**
.457*
.441*
.730**
.711**
.562**
.352*

.000
.720**
.762**


R2
.713**
.236
.359*
.667**
.588**
.540**
.187

.104
.566**
.644**








Table A-3. PolS twr tv


331 .3583 -XO 1047 .453**e
.80: -j: 012 g .07 .166
4 -.189 316* -.156 .337 17
65 -.18* 318* 00532 -.516** .403*"
7~j8 ----- ------ -.371* .344*

9.042 .073 .23733 2
.357* .064 .39 83* .210*
-.0 .3 63 .02.5
-.234 .002 -.240 .168
1 -.32; -.009 -. 91 -.055 .094
.127 -.017 04 -731** .554**
14 .0748 .078 .105 .021 .087
15-.0400* .043 .385" .011 .156
176 .4072 -.27 316526 -.713** .65*
-.210 .242 -131 -.217 .2303
19 -.205 .184 263 -.439* .313*

.073 -.081i -.9069 .183
22-.1023 -.010 245 .549** .373*
23-.1294 173 -.102 -.408* .198
-.248 149 86 -78* .626**

2 6 88" *2 I4 0 3 04* .386
-.443** 268P 0211 .126 .1396
-.180 180 8 -.13* 366*
2 9 .00 90305
32 ------ -------- -13.193 .086
-.144 .155 .12 -.8 .9"
333 .082 .009 .2 .8*
34 221 02 97 202
76 .3223 -4 21 15426 .122 .054
38 ----- -------- -12.491** .416**
.065 -013 7 2 .9

4 9- 4 02 03873 .347
.049 .215 -.3403* .183
4432 .185 -042 -. 04 .787** .669**"











Table A-3. Continued
Participant Age
44 -.108
45 -.181
46 -.473**
47 -.350**
48 -.2787
49 -.454**
50 -.226
51 - -
52 -.134
53 -.240
54 .010
**p<.01,*p<.05, tp<.10


Race
.149
.010
-.108
.091
-.081
-.307*
.111

.370*
-.110
-.169


Sex
.108
-.341*
.028
-.141
-.038
-.043
-.116

.195
-.240
-.129


Pain
-.316
-.366*
-.512**
-.662**
-.568**
-.392**
-.368*

.082
-.386*
-.618**


R2
.146
.283*
.509**
.590**
.408**
.456**
.213

.200
.277t
.427**








Table A-4. Polswrd n


31: -.526* 3245 5 .437**y
-.184 -.03" 24 .032 .338*
54 .049 .272* 0080 -.071 .046
6 ~.521** .201 .10605 .89** .751*"
7 -.060 .018 .02 .632* .722*
8 ~.374** .045 .29265 02.0

19 .439 200 03:,.36
-.2 .0 -1 4.7 .6285*
12-.1343" .2541* .0913 77 .4 *
.16.0 9-d 2 .758** .673**
1588 02 .10 .443* 007852428
76 .038 .0384 .02* .2*
18 .26 .06134 -.0637 .182"
9 .077 -.166 191 .545** .6389**
.118 -.0420; .444 .267


2 ~.262 .1277 0.656** .457**
4 -.20;" .075 .800 .737** .7629*
25 .180 -.075 -.14 .636* .425*
26 20 .255 .122 .600** .43**"

.042 20295 .4191744 .2936 *
2 28 032 1 547 02 80 9
34 .117 -. s011 0 117.68 .424*


09-.1420 .172 _0 42* .4
41-02 7 -.003 .625** 545**
42 ~.100 -204 .096.0** .2*


292










Table A-4. Continued
Participant Age
44 .171
45 .123
46 .408**
47 .336**
48 .248?
49 .096
50 .105
51 - -
52 .169
53 .147
54 -.145
**p<.01,*p<.05, tp<.10


Race
-.014
-.043
-.052
-.034
.047
.183
.156

.216
.032
.101


Sex
.282**
.305?
-.051
.138
.033
.057
-.228

-.005
.015
.261?


Pain
.788**
.376*
.609**
.709**
.603**
.535**
.167

.084
.653**
.630**


R2
.729**
.251t
.543**
.635**
.429**
.332*
.115

.082
.450**
.496**








Table A-5. Polstwr


315 .546* 02208 .056
-.180 -.180*02 .262t .369*
.116 -.1980 180 -.180 .129
6 0816 -.24 -2431 .282 .150
7 .O18 -.180 804 .081 .132
8 ~ ,8 .1XO--- -------- .180 .129
-.037 .213 -----------
0 .200 .035 10 .409* .228
.106 .152 .21 ~ 59 .123
.438* -045 .354 .350 1578
13 .029 -.001 229 .191* .248"
14 .273 -.020 164 .50** .355*

618 8249 .64" 8686 *4
19 .136 .311 6 -31 .225"
20 ~ 01 .220 ------- -.017 .129
2~17 1 0 4 9 7 24 516 6 3
3 01


39 .008 .2597 -16 .069 .092

32 -.180 .180 -180 .168 .013
33 .229 .229 1156 .5180 .3129

.01 o 9 *4 27 .97
-.8 1 0.44.7
36-.511** 9 07-180 -180 .129
3718 -.44- 81 -.112 -.468* .496*
408 037 0001 ~581*.3
-.161 -.132 -.2310 .188
2~00 oo -25 7











Table A-5. Continued
Participant Age Race Sex Pain R2
44 -.128 .222 .222 -. 128 .131
45 -.229 -.200 .135 -.232 .165
46 -.237 -.081 .133 -.352t .214
47 -.334* .116 -.040 -.618** .509**
48 -.348* -.100 -.016 -.557** .442**
49 -.391* -.014 -.251 -.334* .327*
50 .089 .164 -.202 -.089 .083
51 -.180 -.180 .180 .180 .129
52 .150 -.354* -. 150 -. 107 .182
53 .269t .103 .024 .515** .349*
5 4-- - -- - -- - -- - -- - -
**p<.01,*p<.05, tp<.10








Table A-6. Po


.18 .180" 8003 .199 .226
.106 217* -10.180 .129
65.3678 0276 00904 ~85 .818**
-------- ___ 0 8 48 .397**
201 -402* .126 11.2
9 210 4907949 .370* 540* .423298
10 .0640 131 .155 .174 .199
.0240 -142 .180 .454** .257t
32 -.002 .084 20896 665** .470**
14 .331 .i085 2991 .740** .603**
1 5 00 1 2 .343" 3167 323*
76 .008 040 03738 .776** .622**
.252 .200 -.33040 .057
18 .040 -.181 .21.3611 .489*
20.0989 3094.4 q5456 .3 *
21 -.278* 312* .083 .000 .123
22 _28 32 .141 .626** .587**
23 .302** -.077 .11- --------3"
54 -.112 -.007 -896 76* .1*
26 8841189 .417* ~ 249 *5375

29 .349* 170 .151 .268 .173
33.3089 135 632 **.611** .632**
31---- _0 -15.01.020 .031
32 3 ------
~.33 -.0940098* .003 .512**.20
3354 .008 -159* 6 .060 .259t
36 .107 -322: -107 .175* .598*
37 17574 8434 .248 .3027 .3 98
33 .102 139* 1583 .177 .129
40 .1026 .096" -411* 497** .438**
40 .124 .333** 11 .236 .235
41 -.106 -.025" .131 .730** .676**
32~0 -.027 .8 04397 .920** .860**










Table A-6. Continued
Participant Age Race Sex Pain R2
44 -.151 -.151 .001 .189 .081
45 .031 .305t .209 .279 .216
46 .135 .144 -.050 .360t .180
47 .341** -.086 .125 .717** .653**
48 .182 .158 .006 .594** .411**
49 .337* .049 .141 .447** .335*
50 .298 -.053 .165 -.074 .124
51 .180 .180 -.180 -. 180 .129
52 .147 -.332t -.147 -. 114 .166
53 .305* -.065 .033 .656** .528**
54 .002 .034 .297* .625** .480**
**p<.01,*p<.05, tp<.10








TableA-7. Pol~~ad ca


.298 -.007 .194~g .174
5 ~.320 .110 -.047 .040 .093
76 .019 .119 99 320.23
817 -.307 .040 .268 .100
-.093.7t 940 -.360* .256t
9-.464* .282 .252 .180 .143
10 .008* .0082 -001 .145 .316*
-.078 .287 .133 -.174 .048
.04.07~87 I 88 .511** .357*
.3541 .080 2019 774** .618**
15 .156 -.16 499* -011 .167
16 .216 -.076 .234 -.110* .4312*
17 .030 .030 355* 3579 .443
19 -- -
201 8.6. .147 53"* .4 *



2 0

35 -.11 -.144 202 .159 .072
22 .180 21t 356**x -.098 22

4127 -.8 -10 10 -.13 .1408
-.184 -.30*7 .180~4 .1298

42 .336* .237 .074 .657" .560*










Table A-7. Continued
Participant Age
44 .177
45 -.167
46 -.109
47 -.209
48 -.370*
49 -.280
50 -.152
51 - -
52 .190
53 .115
54 .257
**p<.01,*p<.05, tp<.10


Race
-.149
-.237
.110
.195
-.108
-.280
.281

.224
-.187
-.244


Sex
-.012
.073
.031
-.086
.042
-.038
.094

-.184
.209
.244


Pain
.152
-.261
-.270
-.540**
-.500**
-.082
-.3047

.034
.587**
.068


R2
.077
.157
.096
.381**
.400**
.165
.203

.121
.436**
.190








Table A-8.P~olSwrd c


.222 146 .27027 .101
54 .057 .065 00579 .349* 20
----------- .7 9** .633**e

6 .051 .107 00---------8
.064 -.295 .01 .264 .084
.083 .124 -.218q7 .138t
0O .303' -.21 9865 .447 .257
.004 .162 131 -.096 .213
-.051 .182 .05 403* .206t
12 .411.0546* 25
.28 .161 0808* .703** .592**
.192 .304 .0 .157 .108
65.049 -.049 3004 .155 .2457
17 -.063 .032 3171 .824* .168*
18 .180 -.180 .180 -.101 .106
19 --- ---- 1 0.2
22 1 2.9 365 50" 031
22-.3;46 .277 159 -.040 .077
-.027 .3117t .5255 .287*
2 .028* -.01' -030 .252 .162
2254 .038" .013 -298 .797** .731**


30 .109 .199 .253 .335* .2306
986-0 50
33 -.034 393* .118T .195 .073

76 -.036 -.039 7801 .180 .012
28 .077 .2109.4 .5167* 49*
39 .0659 .075 .258 .413* .255



.1100










Table A-8. Continued
Participant Age Race Sex Pain R2
44 .320t .000 .160 .320t .231
45 -.128 .151 .105 .206 .093
46 -.098 .306t -.219 .281 .241
47 .392** -.073 .083 .652** .592**
48 .154 .184 -.011 .563** .374*
49 .122 .267 -.071 .361* .222
50 .409* -.027 .264 -.002 .237
51 -.180 .180 .180 -. 180 .129
52 .091 .310t -. 125 .100 .130
53 .090 .167 .070 .583** .381**
54 -.261t .197 .146 .501** .380**
**p<.01,*p<.05, tp<.10










LIST OF REFERENCES


Acute Pain Management Guideline Panel. Acute pain management: Operative or medical
procedures and trauma. Clinical practice Guideline. (1992). Rockville, MD: Agency for
Health Care Policy and Research, Public Health Service, U. S. Department of Health and
Human Services.

Agency for Health Care Policy and Research. Management of Cancer Pain. (1994). Rockville,
MD: Agency for Health Care Policy and Research, Public Health Service, U.S.
Department of Health and Human Services.

Ahles, T. A., Coombs, D. W., Jensen, L., Stukel, T., Maurer, L. H., & Keefe, F. J. (1990).
Development of a behavioral observation technique for the assessment of pain behaviors
in cancer patients. Behavior Therapy, 21, 449-460.

Alspach, J. G. (2000). From staff nurse to preceptor: A preceptor development program (2nd
ed.). Aliso Viejo, CA: American Association of Critical Care Nurses.

American Geriatrics Society Panel on Persistent Pain in Older Persons. (2002). The management
of persistent pain in older persons. Journal of the American Geriatrics Society, 50,
205-224.

American Medical Association. (2005). Physician characteristics and distribution in the U.S.
Chicago, 1L: American Medical Association.

American Pain Society. (1992). Principles ofanalgesic use in the treatment of acute pain and
chronic cancer pain (3rd ed.). Skokie, IL: American Pain Society.

Anderson, G., & Horvath, J. (2002). Chronic conditions: Making the case for ongoing care.
Princeton, NJ: Robert Wood Johnson Foundation's Partnership for Solutions.

Anderson, K. O., Mendoza, T. R., Valero, V., Richman, S. P., Russell, C., Hurley, J., et al.
(2000). Minority cancer patients and their providers: pain management attitudes and
practice. Cancer, 88, 1929-1938.

Aubrun, F. (2005). Management of postoperative analgesia in elderly patients. Regional
Anesthesia\k and Pain M~edicine, 30, 363-379.

Auret, K., & Schug, S. A. (2005). Underatilisation of opioids in elderly patients with chronic
pain: Approaches to correcting the problem. Drugs & Aging. 22, 641-654.

Bartfield, J. M., Salluzzo, R. F., Raccio-Robak, N., Funk, D. L., & Verdile, V. P. (1997).
Physician and patient factors influencing the treatment of low back pain, Pain, 73,
209-211.

Beal, D., Gillis, J. S., & Stewart, T. (1978). The lens model: Computational procedures an
applications. Perceptual & M~otor Mills//, 46, 3-28.










Bellville, J. W., Forrest, W. H., Miller, E., & Brown, B. W. (1971). Influence of age on pain
relief from analgesics. JAM~A, 217, 1835-1841.

Bernabei, R., Gambassi, G., Lapane, K., Landi, F., Gatsonis, C., Dunlop, R., et al. (1998).
Management of Pain in Elderly Patients with Cancer. JAM4A, 279, 1877-1882.

Beyer, J. E., DeGood, D. E., Ashley, L. C., & Russell, G. A. (1983). Patterns of postoperative
analgesic use with adults and children following cardiac surgery. Pain, 17, 71-81.

Bier, M. (1990). A comparison of the degree of racism, sexism, and homophobia between
beginning and advancedpsychology students. Unpublished master's thesis, East Carolina
University, Greenville, NC.

Bond, M. R. (1971). Pain in hospital. Lancet, 1, 37.

Bond, M. R., & Pilowsky, I. (1966). Subj ective assessment of pain and its relationship to the
administration of analgesics in patients with advanced cancer. Journal Psychosontatic
Research, 10, 203-208.

Borod, J. C., Yecker, S. A., Brickman, A. M., Moreno, C. R., Sliwinski, M., Foldi, N. S., Alpert,
M., Welkowitz, J. (2004). Changes in posed facial expression of emotion across the adult
life span. ExperintentalAging Research, 30, 305-33 1.

Breau, L. M., McGrath, P. J., Craig, K. D., Santor, D., Cassidy, K-L., & Reid, G. J. (2001).
Facial expression of children receiving immunizations: A principal components analysis
of the child facial coding system. Clinical Journal ofPain, 1 7, 178-186.

Breitbart, W., McDonald, M. V., Rosenfeld, B., Passik, S. D., Hewitt, D., Thaler, H., et al.
(1996). Pain in ambulatory AIDS patients. I. Pain characteristics and medical correlates.
Pain, 68, 315-321.

Buchan, J. (2005) A certain ratio? The policy implications of minimum staffing ratios in nursing.
Journal of Health Services Research and Policy, 10, 239-247.

Calderone, K. L. (1990). The influence of gender on the frequency of pain and sedative
medication administration to postoperative patients. Sex Roles, 23, 713-725.

Campbell, L. C. (2002). Predispositions toward pharmacological pain management: A policy
capturing study (Doctoral Dissertation, University of Florida, 2002). Dissertation
Abstracts International, 63, 4892.

Campbell, C. M., Edwards, R. R., & Fillingim, R. B. (2005). Ethnic differences in responses to
multiple experimental pain stimuli. Pain, 113, 20-26.

Casanova, J., Day, K., Dorpat, D., Hendricks, B., Theis, L., & Wiesman, S. (2007). Nurse-
physician work relations and role expectations. Journal of Nursing Administration, 3 7,
68-70.










Chakour, M. C., Gibson, S. J., Bradbeer, M., & Helme, R. D. (1996). The effect of age on A-
delta and C fibre thermal pain perception. Pain, 64, 143-152.

Choiniere, M. Melzack, R., Girard, N., Rondequ, J., & Paquin, M. J. (1990). Comparisons
between patients' and nurses' assessment of pain and medication efficacy in severe burn
injuries. Pain, 40, 143-52.

Cleeland, C. S., Gonin, R., Baez, L., Loehrer, P., & Pandya, K. J. (1997). Pain and treatment of
pain in minority patients with cancer. Annals oflnternal2\~edicine, 127, 813-816.

Cleeland, C. S., Gonin, R., Hatfield, A. K., Edmonson, J. H., Blum, R. H., Stewart, J. A., et al.
(1994). Pain and its treatment in outpatients with metastatic cancer. NEJM, 330, 592-596.

Cohen, F. L. (1980). Postsurgical pain relief: Patients' status and nurses' medication choices.
Pain, 9, 265-74.

Cohen-Mansfield, J., & Lipson, S. L. (2002a). Pain in cognitively impaired nursing home
residents: How well are physicians diagnosing it? Journal of the American Geriatrics
Society, 50, 1039-1044.

Cohen-Mansfield, J., & Lipson, S. (2002b). The underdetection of pain of dental etiology in
persons with dementia. American Journal ofAlzheimer 's Disease and Other Dementias,
17, 249-253.

Cook, A. K., Niven, C., & Downs, M. G. (1999). Assessing the pain of people with cognitive
impairment. International Journal of Geriatric Psychiatry, 14, 42 1-425.

Cooksey, R. W. (1996). Judgment analysis: Theory, methods, and applications. San Diego, CA:
Academic Press.

Cousins, M. J. (1991). Prevention of postoperative pain. In M. R. Bond, J. E. Charlton, & C. J.
Woolf (Eds.), Pain research and clinical management: Vol. 4. Proceedings of the 6th
World Congress on Pain. (pp. 41-50). New York, NY: Elsevier

Cousins, M. (1994). Acute and postoperative pain. In P. Wall, & R. Melzack (Eds.), Textbook of
pain. (pp. 357-385). Edinburgh, Scotland: Churchill Livingstone.

Craig, K. D. (1980). Ontogenetic and cultural influences on the expression of pain in man. In H.
W. Kosterlitz, & L. Y. Terenius (Eds.), Pain and society. (pp. 39-52). Weinheim: Verlag
Chemie.

Craig, K. D. (1992). The facial expression of pain: better than a thousand words? APS Journal, 1,
153-162.

Craig, K. D., Hyde, S. A., & Patrick, C. J. (1991). Genuine, suppressed and faked facial behavior
during exacerbation of chronic low back pain. Pain, 46, 161-171.










Craig, K. D., & Prkachin, K. M. (1983). Nonverbal measures of pain. In R. Melzack (Ed.), Pain
measurement and assessment. (pp. 173-179). New York: Raven Press.

Craig, K. D., Prkachin, K. M., & Grunau, R. (1992). The facial expression of pain. In D. Turk, &
R. Melzack (Eds.), Handbook ofpain assessment. (pp. 257-276). New York: Guilford
Press.

Craig, K. D., Whitfield, M. F., Grunau, R. V. E., Linton, J., & Hadjistavropoulos, H. D. (1993).
Pain in the preterm neonate: Behavioural and physiological indices. Pain, 52, 287-299.

Creamer, P., Lethbridge-Cejku, M., & Hochberg, M. C. (1999). Determinants of pain severity in
knee osteoarthritis: Effect of demographic and psychosocial variables using 3 pain
measures. Journal ofRheumatology, 26, 1785-1792.

Darwin, C. (1872/1965). The expression of the emotions in man and animals. Chicago:
University of Chicago Press. (Original work published 1872)

van Dongen, K. A. J., Abu-Saad, H. H., & Hamers, J. P. H. (1999). On the development of an
observational scale to measure pain in nonverbal children with severe or profound
cognitive impairment; collecting the indicators. Proceedings of the 9th World Congress
ofPain. (p. 87). Seattle: IASP Press.

Edwards, R. R., Fillingim, R. B., & Ness, T. J. (2003). Age-related differences in endogenous
pain modulation: A comparison of diffuse noxious inhibitory controls in healthy older
and younger adults. Pain, 101, 155-165.

Edwards, R. R., & Fillingim, R. B. (1999). Ethnic differences in thermal pain responses.
Psychosomatic M~edicine, 61, 3 46-3 54.

Ekman, P. (1992). An argument for basic emotions. Cognition and Emotion, 6, 169-200.

Ekman, P. (1994). Strong evidence for universals in facial expressions: A reply to Russell's
mistaken critique. Psychological Bulletin, 113, 268-287.

Ekman, P., & Friesen, W. V. (1969). The repertoire of nonverbal behavior: Categories, origins,
usage, and coding. Semiotica, 1, 49-98.

Ekman, P., & Friesen, W. V. (1978). Manual for the facial action coding system. Palo Alto, CA:
Consulting Psychology Press.

Ekman, P., & Friesen, W. V. (1971). Constants across cultures in the face and emotion. Journal
ofPersonality and Social Psychology, 17, 124-129.

Ekman, P., Friesen, W. V., & Ellsworth, P. (1983). What components of facial behavior are
related to observers' judgments of emotion? In P. Ekman (Ed.), Emotion in the human
face. (pp. 98-110). Cambridge, England: Cambridge University Press.










Ekman, P., Friesen, W. V., O' Sullivan, M., Chan, A., Diacoyanni-Tarlatzis, I., Heider, K., et al.
(1987). Universals and cultural differences in the judgments of facial expressions of
emotion. Journal ofPersonality and Social Psychology, 53, 712-717.

Etcoff, N. L. & Magee, J. J. (1992). Categorical perception of facial expression. Cognition, 44,
227-240.

Faherty, B. S., & Grier, M. R. (1984). Analgesic medication for elderly people post-surgery.
Nursing Research, 33, 369-372.

Faucett, J., Gordon, N., & Levine, J. (1994). Differences in postoperative pain severity among
four ethnic groups. Journal of Pain Synapton; Management, 9, 383-389.

Feldt, K. S., Ryden, M. B., & Miles, S. (1998). Treatment of pain in cognitively impaired
compared with cognitively intact older patients with hip-fracture. Journal of the
American Geriatrics Society, 46, 1079-1085.

Ferrell, B. A. (1995). Pain evaluation and management in the nursing home. Annals oflnternal
Medicine, 123, 68 1-687.

Ferrell, B. A. (1996). Overview of aging and pain. In B. R. Ferrell, & B. A. Ferrell (Eds.), Pain
in the elderly. (pp. 1-10). Seattle, WA: IASP Press.

Ferrell, B. A. (2003). Acute and chronic pain. In C. K. Cassel, R. M. Leipzig, H. J. Cohen, E. B.
Larson, & D. E. Meier (Eds.), Geriatric medicine: An evidence-based approach. (pp.
323-342). New York, NY: Springer-Verlag.

Ferrell, B. A., Ferrell, B. R., & Rivera, L. (1995). Pain in cognitively impaired nursing home
patients. Journal of Pain and Synapton; Management, 10, 591-599.

Ferrell, B. R., McGuire, D. B., & Donavan, M. I. (1993). Knowledge and beliefs regarding pain
in a sample of nursing faculty. Journal of Professional Nursing, 9, 79-88.

Fitzpatrick, J. J. (2007). Cultural competence in nursing education revisited. Nursing Education
Perspectives, 28, 5.

Foster, M. C., Pardiwala, A., & Calthorpe, D. (2000). Analgesia requirements following hip
fracture in the cognitively impaired.1Injury, 31, 435-436.

Fridlund, A. J. (1994). Human facial expression: An evohitionary view. San Diego, CA:
Academic Press.

Fries, B. E., Simon, S. E., Morris, J. N., Flodstrom, C., & Bookstein, F. L. (2001). Pain in U.S.
nursing homes: Validating a pain scale for the Minimum Data Set. The Gerontologist, 41,
173-179.

Fuentes, E. F., Kohn, M. A., & Neighbor, M. L. (2002). Lack of association between patient
ethnicity or race and facture analgesia. Academic Emergency M~edicine, 9, 910-915.










Gagliese, L. (2001). Assessment of pain in elderly people. In D. C. Turk, & R. Melzack (Eds.),
Handbook ofpain assessment. (pp. 119-133). New York, NY: Guilford Press.

Gagliese, L., Jackson, M., Ritvo, P., Wowk, A., & Katz, J. (2000). Age is not an impediment to
effective use of patient controlled analgesia by surgical patients. Anothel~ill~ Sithoy 93,
601-610.

Gagliese, L., & Katz, J. (2003). Age differences in postoperative pain are scale dependent: A
comparison of measures of pain intensity and quality in younger and older surgical
patients. Pain, 103, 11-20.

Girard, N. J. (2003). Men and nursing. AORNJournal, 77, 728-730.

Gloth, F. M. (2000). Geriatric pain. Factors that limit pain relief and increase complications.
Geriatrics, 55, 46-54.

Goldberg, M. A., & Remy-St. Louis, G. R. (1998). Understanding and treating pain in ethnically
diverse patients. Journal of Clinical Psychology in M~edical Settings, 5, 343-356.

Goodenough, B., Addicoat, L., Champion, G. D., McInerney, M., Young, B., Juniper, K., et al.
(1997). Pain in 4- to 6-year-old children receiving intramuscular immunization inj sections:
A comparison of the Faces Pain Scale with other self-report and behavioral measures.
Clinical Journal ofPain, 13, 60-73.

Grunau, R. V. E., & Craig, K. D. (1987). Pain expression in neonates: Facial action and cry.
Pain, 28, 395-410.

Hadjistavropoulos, H. D., & Craig, K. D. (1994). Acute and chronic low back pain: cognitive,
affective, and behavioral dimensions. Journal of Consulting and Clinical Psychology, 62,
341-349.

Hadjistavropoulos, T., LaChapelle, D. L., Hadjistavropoulos, H. D., Green, S., & Asmundson, G.
J. G. (2002). Using facial expressions to assess musculoskeletal pain in older persons.
European Journal ofPain, 6, 179-187.

Haidt, J., & Keltner, D. (1999). Culture and facial expression: Open-ended methods find more
expressions and a gradient of recognition. Cognition and Emotion, 13, 225-266.

Hale, C., & Hadjistavropoulos, T. (1997). Emotional components of pain. Pain Research and
Management, 2, 217-225.

Hardy, R., & Smith, R. (2001). Enhancing staff development with a structured preceptor
program. Journal of Nursing Care Quality, 15, 9-17.

Harkins, S. W. (1996). Geriatric pain. Pain perceptions in the old. Clinics in Geriatric M~edicine,
12, 435-459.










Helme, R. D., & Gibson, S. J. (1997). Pain in the elderly. In T. S. Jensen, J. A. Turner, & Z.
Wiesenfeld-Hallin (Eds.), Proceedings of the 8th World Congress on Pain: Progress in
pain research and management. (pp. 919-944). Seattle, WA: IASP Press.

Herr K. (2002). Chronic pain: Challenges and assessment strategies. Journal of Gerontological
Nursing, 28, 20-27.

Herr, K. A., & Mobily, P. R. (1991). Complexities of pain assessment in the elderly. Clinical
considerations. Journal of Gerontological Nursing, 1 7, 12-9.

Hoffman, C., Rice, D., & Sung, H. Y. (1996). Persons with chronic conditions: Their prevalence
and costs. JAM4A, 276, 1473-1479.

Holm, K., Cohen, F., Dudas, S., Medema, P., & Allen, B. (1989). Effect of personal pain
experience on pain assessment. Journal of Nursing Scholarship, 21, 72-75.

Horgas, A. L., & Elliott, A. F. (2004). Pain assessment and management in persons with
dementia. Nursing Clinics of North America, 39, 593-606.

Horgas, A. L., & Tsai, P. F. (1998). Analgesic drug prescription and use in cognitively impaired
nursing home residents. Nursing Research, 47, 235-242.

Hughes, K. H., & Hood, L. J. (2007). Teaching methods and an outcome tool for measuring
cultural sensitivity in undergraduate nursing students. Journal of Transcultural Nursing,
18, 57-62.

International Association for the Study of Pain. (1993). Curriculum on pain for schools of
nursing. Seattle, WA: Intemnational Association for the Study of Pain.

International Association for the Study of Pain. (1997). Curriculum on pain for schools of
nursing. Seattle, WA: Intemnational Association for the Study of Pain.

Irvine, D., Sidani, S., Porter, H., O'Brien-Passal, L., Simpson, B., McGillis Hall, L., Graydon, J.,
DiCenso, A., Redelmeir, D., & Nagel, L. (2000). Organizational factors influencing nurse
practitioners' role implementation in acute care settings. Canadian Journal of Nursing
Leadership, 13, 28-35.

Johnson, M. K., & Marini, M. M. (1998). Bridging the racial divide in the United States: The
effect of gender. Social Psychology Quarterly, 61, 247-258.

Kaasalainen, S. J., Robinson, L. K., Hartley, T., Middleton, J., Knezacek, S., & Ife, C. (1998).
The assessment of pain in the cognitively impaired elderly: A literature review.
Perspectives, 22, 2-8.

Karani, R., & Meier, D. E. (2004). Systemic pharmacologic postoperative pain management in
the geriatric orthopaedic patient. Clinical Oi Iluspe~l~ iL a ~nd Rela.ted Resea~rch, 425,
26-34.










Karpman, R. R., Del Mar, N., & Bay, C. (1997). Analgesia for emergency centers' orthopaedic
patients: Does an ethnic bias exist? Clinical O; thoped~l'~ iL a nd Rela.ted Resea~rch, 334,
270-275.

LaChapelle, D. L., Hadjistavropoulos, T., & Craig, K. D. (1999). Pain measurement in persons
with intellectual disabilities. Clinical Journal ofPain, 15, 13-23.

Lavsky-Shulan, M., Wallace, R. B., Kohout, F. J., Lemke, J. H., Morris, M. C., & Smith, I. M.
(1985). Prevalence and functional correlates of low back pain in the elderly: the Iowa 65+
Rural Health Study. Journal of the American Geriatrics Society, 33, 23-28.

LeResche, L. (1982). Facial expression in pain: a study of candid photographs. Journal of
Nonverbal Behavior, 7, 46-56.

LeResche, L., & Dworkin, S. F. (1988). Facial expression of pain and emotions in chronic TMD
patients. Pain, 35, 71-78.

Lilley, C. M., Craig, K. D., & Grunau, R. V. E. (1996). Rating the intensity of facial actions in
infants and toddlers: Impact on effect size. Abstracts of the 8th World Congress on Pain.
Seattle, WA: IASP Press.

Lindh, V., Wiklund, U., Sandman, P. O., & Hakansson, S. (1997). Assessment of acute pain in
preterm infants by evaluation of facial expression and frequency domain analysis of heart
rate variability. Early Human Development, 48, 13 1-142.

Lipson, J. G., & DeSantis, L. A. (2007). Current approaches to integrating elements of cultural
competence in nursing education. Journal of Transcultural Nursing, 18, 10S-20S.

Malatesta, C., Izard, C. E., Culver, C., & Nicolich, M. (1987). Emotion communication skills in
young, middle-aged and older women. Psychology and Aging. 2, 193-203.

McCaffery, M., & Ferrell, B. R. (1992). Does the gender gap affect your pain management
decisions? Nursing 92, 22, 48-51.

McDonald, D. D. (1994). Gender and ethnic stereotyping and analgesic administration. Research
in Nursing & Health, 17, 5-49.

McDonald, D. D., & Bridge, R. G. (1991). Gender stereotyping and nursing care. Research in
Nursing & Health, 14, 373-378.

Melzack, R. (1975). The McGill Pain Questionnaire: maj or properties and scoring methods.
Pain, 1, 277-299.

Merskey, H., & Bogduk, N. (1994). Classif ication of chronic pain: Descriptions of chronic pain
syndromes and definitions of pain terms (2n4 ed.). (1994). Seattle, WA: International
Association for the Study of Pain.










Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity
for processing information. Psychological Review, 63, 81-97.

Mobily, P. R., Herr, K. A., Clark, M. K., & Wallace, R. B. (1994). An epidemiologic analysis of
pain in the elderly: The Iowa 65+ Rural Health Study. Journal of Aging and H~ealth, 6,
139-154.

Montamat, S. C., Cusack, B. J., & Vestal, R. E. (1989). Management of drug therapy in the
elderly. NEJ~f, 321, 303-309.

Moore, A. R., & O'Keeffe, S. T. (1999). Drug-induced cognitive impairment in the elderly.
Drugs & Aging. 15, 15-28.

Moreno, C., Borod, J., Welkowitz, J., & Alpert, M. (1993). The perception of facial emotion
across the adult life span. Developmental Neuropsychology, 9, 305-314.

Morgan, J., & Puder, K. (1989). Postoperative analgesia: Variations in prescribed and
administered opioid dosages. In C. S. Hill, & W. S. Fields (Eds.), Ad'vances in pain
research and' therapy. (pp. 175-180). New York, NY: Raven.

Morrison, R. S., Magaziner, J., Gilbert, M. Koval, K. J., McLaughlin, M. A., Orosz, G., et al.
(2003). Relationship between pain and opioid analgesics on the development of delirium
following hip fracture. Journals of Gerontology Series A: Biological Sciences and'
Medical Sciences, 58, 76-8 1.

Nelson, D. V., Novy, D. M., Averill, P. M., & Berry, L. A. (1996). Ethnic comparability of the
MMPI in pain patients. Journal of Clinical Psychology, 52, 485-497.

Ng, B., Dimsdale, J. E., Rollnik, J. D., & Shapiro, H. (1996a). The effect of ethnicity on
prescriptions for patient controlled analgesia for post-operative pain. Pain, 66, 9-12.

Ng, B., Dimsdale, J. E., Shragg, G. P., & Deutsch, R. (1996b). Ethnic Differences in Analgesic
Consumption for Postoperative Pain. Psychosomatic M~edicine, 58, 125-29.

Nishikawa, S. T., & Ferrell, B. A. (1993). Pain assessment in the elderly. Clinical Geriatrics and'
Issues in Long Term Care, 1, 15-28.

Oberle, K., Paul, P., Wry, J., & Grace, M. (1990). Pain, anxiety and analgesics: A comparative
study of elderly and younger surgical patients. Canad'ian Journal on Aging. 9, 13-22.

Owen, H. Szekeley, J., Plummer, J., Cushnie, J., & Mather, L. (1989). Variations in patient
controlled analgesia 2: Concurrent infusion. Awlbliall~k l 44, 11-13.

Parmelee, P. A., Katz, I. R., & Lawton, M. P. (1991). The relation of pain to depression among
institutionalized aged. Journal of Gerontology, 46, 15-21.

Pilowsky, I., & Bond, M. R. (1969). Pain and its management in malignant disease. Elucidation
of staff-patient transactions. Psychosomatic M~edicine, 31, 400-404.










Pollard, K. (2003). Searching for autonomy. M~idwifery, 19, 113-124.


Poole, G. D., & Craig K. D. (1992). Judgments of genuine, suppressed and faked facial
expressions of pain. Journal ofPersonality and Social Psychology, 63, 797-805.

Popp, B., & Portenoy, R. K. (1996). Management of chronic pain in the elderly: Pharmacology
of opioids and other analgesic drugs. In B. R. Ferrell, & B. A. Ferrell (Eds.), Pain in the
elderly. (pp. 21-34). Seattle, WA: IASP Press.

Portenoy, R. K. (1996). Opioid therapy for chronic nonmalignant pain: a review of the critical
issues. Journal of Pain and Synapton; Management, 11, 203-217.

Prkachin, K. M. (1992). The consistency of facial expressions of pain: A comparison across
modalities. Pain, 51, 297-306.

Prkachin, K. M., Berzins, S. & Mercer, S. R. (1994) Encoding and decoding of pain expressions:
A judgment study. Pain, 58, 253-259.

Prkachin, K. M., & Mercer, S. R. (1989). Pain expression in patients with shoulder pathology:
validity, properties and relationship to sickness impact. Pain, 39, 257-265.

Prkachin, K. M., Solomon, P., Hwang, T., & Mercer, S. R. (2001). Does experience influence
judgments of pain behavior? Evidence from relatives of pain patients and therapists. Pain
Research and Management, 6, 105-112.

Qualls, R. C., Cox, M. B., & Schehr, T. L. (1992). Racial attitudes on campus: Are there gender
differences? Journal of College Student Developnzent, 33, 524-530.

Robinson, J. H. (2000). Increasing students' cultural sensitivity. A step toward greater diversity
in nursing. Nurse Education, 25, 131-135.

Robinson, M. E., & Wise, E. A. (2003). Gender bias in the observation of experimental pain.
Pain, 104, 259-264.

Robinson, M. E., Riley III, J. L., Myers, C. D., Papas, R. K., Wise, E. A., Waxenberg, L. B., et
al. (2001). Gender role expectations of pain: Relationship to sex differences in pain. The
Journal ofPain, 2, 251-257.

Robinson, M. E., & Riley, J. L. (1998). Negative emotion in pain. In R. Gatchel, & D. Turk
(Eds.), Psychosocial factors in pain. (pp. 74-88). New York: Guilford Press.

Rooke, G. A., Reves, J. G., & Rosow, C. (2002). Anesthesiology and geriatric medicine


Ross, H. (2000). Lifting the unequal burden of cancer on minorities and the underserved.
Closing the gap. Washington, DC: Office of Minority Health, U. S. Department of Health
and Human Services.










Russell, J. A. (1994). Is there universal recognition of emotion from facial expression? A review
of cross-cultural studies. Psychological Bulletin, 115, 102-141.

Russell, J. A. (1995). Facial expressions of emotion: What lies beyond minimal universality?
Psychological Bulletin, 118, 379-391.

Salmon, P., & Manyande, A. (1996). Good patients cope with their pain: postoperative analgesia
and nurses' perceptions of their patients' pain. Pain, 68, 63-68.

Sambamoorthi, U., Walkup, J., McSpiritt, E., Warner, L., Castle, N., & Crystal, S. (2000). Racial
differences in end-of-life care for patients with AIDS. AIDS Public Policy Journal, 15,
136-148.

Sarkisian, C. A., Hays, R. D., Berry, S. H., & Mangione, C. M. (2001). Expectations regarding
aging among older adults and physicians who care for older adults. Medical Care, 39,
1025-1036.

Schuman, H., Steeh, C., & Bobo, L. (1997). Racial attitudes in America: Trends and
interpretations. Cambridge, MA: Harvard Univ. Press.

Sengstaken, E. A., & King, S. A. (1993). The problems of pain and its detection among geriatric
nursing home residents. Journal of the American Geriatrics Society, 41, 541-544.

Shamian, J., & Inhaber, R. (1985). The concept and practice of preceptorship in contemporary
nursing: A review of pertinent literature. International Journal of Nursing Studies, 22,
79-85.

Sheiner, E. K., Sheiner, E., Shoham-Vardi, I., Mazor, M., & Katz, M. (1999). Ethnic differences
influence care giver' s estimates of pain during labour. Pain, 81, 299-305.

Sherwood, M. B., Garcia-Siekavizza, A., Meltzer, M. I., Hebert, A., Burns, A. F., & McGorray,
S. (1998). Glaucoma's impact on quality of life and its relation to clinical indicators.
Opthalmology, 105, 561-566.

Speers, A., Strzyzewski, N., & Ziolkowski, L. (2004). Preceptor preparation: An investment in
the future. Journal for Nurses in Staff Development, 20, 127-133

Stevens, B. J., Johnston, C. C., & Horton, L. (1994). Factors that influence the behavioral pain
responses of premature infants. Pain, 59, 101-109.

Stewart, W. F., Lipton, R. B., & Liberman, J. (1996). Variation in migraine prevalence by race.
Neurology, 47, 52-59.

Tamayo-Sarver, J. H., Dawson, N. V., Hinze, S. W., Cydulka, R. K., Wigton, R. S., Albert, J.
M., et al. (2003a). The effect of race/ethnicity and desirable social characteristics on
physicians' decisions to prescribe opioid analgesics. Academic Emergency M~edicine, 10,
1239-1248.










Tamayo-Sarver, J. H, Hinze, S. W., Cydulka, R. K., & Baker, D. W. (2003b). Racial and ethnic
disparities in emergency department analgesic prescription. American Journal of Public
Health, 93, 2067-2073.

Teno, J. M., Weitzen, S., Wetle, T., & Mor, V. (2001). Persistent pain in nursing home residents.
JAM4A, 285, 208 1.

Teske, K., Daut, R. L., & Cleeland, C. S. (1983). Relationships between nurses' observations and
patients' self-reports of pain. Pain, 16, 289-296.

Thomas, T., Robinson, C., Champion, D., McKell, M., & Pell, M. (1998). Prediction and
assessment of the severity of post-operative pain and of satisfaction with management.
Pain, 75, 177-185.

Todd, K. H., Deaton, C., D'Adamo, A. P., & Goe, L. (2000). Ethnicity and analgesic practice.
Annals of Emergency M~edicine, 35, 1 1-16.

Todd, K. H., Lee, T., & Hoffman, J. R. (1994). The effect of ethnicity on physician estimates of
pain severity in patients with isolated extremity trauma. JAM4A, 271, 925-928.

Todd, K. H., Samaroo, N., & Hoffman, J. R. (1993). Ethnicity as a risk factor for inadequate
emergency department analgesia. JAM~A, 269, 1537-1539.

Turk, D. C., & Okifuji, A. (1997). What factors affect physicians' decisions to prescribe opioids
for chronic noncancer pain patients? Clinical Journal ofPain, 13, 330-336.

Turk, D. C., & Okifuji, A. (1999). Does sex make a difference in the prescription of treatments
and the adaptation to chronic pain by cancer and non-cancer patients? Pain, 82, 139-148.

Underwood, S. M. (2006). Culture, diversity, and health: responding to the queries of inquisitive
mind s. Journal ofNursing Education, 45, 28 1-286.

Walsh, N., Schoenfeld, L., Ramamurth, S., & Hoffman, J. (1989). Normative model for the cold
pressor test. American Journal ofPhysical M~edicine and Rehabilitation, 68, 6-11.

Watt-Watson, J. H., Evernden, C., & Lawson, C. (1990). Parents' perceptions of their child's
acute pain experience. Journal of Pediatric Nursing, 5, 344-349.

Weiner, D., Peterson, B., & Keefe, F. (1999). Chronic pain-associated behaviors in the nursing
home: Resident versus caregiver perceptions. Pain, 80, 577-588.

Werner, P., Cohen-Mansfield, J., Watson, V., & Pasis, S. (1998). Pain in participants of adult
day care centers: Assessment by different raters. Journal of Pain and' Symptom
Management, 15, 8-17.

White, K. E., & Cummings, J. E. (1997). Neuropsychiatric aspects of Alzheimer's disease and
other dementing illnesses. In S. C. Yudofsky, & R. E. Hale (Eds.), The American










Psychiatric Press textbook ofneuropsychiatry. (pp. 823-854). Washington, DC:
American Psychiatric Press.

Williamson, G. M., & Schulz, R. (1992). Pain, activity restriction, and symptoms of depression
among community-residing elderly adults. Journal of Gerontology, 47, 367-372.

Wise, E. A., Price, D. D., Myers, C. D., Heft, M. W., & Robinson, M. E. (2002). Gender role
expectations of pain: Relationship to experimental pain perception. Pain, 96, 335-342.

Wolff, J. L., Starfield, B., & Anderson, G. (2002). Prevalence, expenditures, and complications
of multiple chronic conditions in elderly. Archives oflnternal2\~edicine, 162, 2269-2276.

World Health Organization. (1986). Cancer Pain. Geneva: World Health Organization.

Wuensch, K. L., Campbell, M. W., Kesler, F. C., & Moore, C. H. (2002). Racial bias in
decisions made by mock jurors evaluating a case of sexual harassment. The Journal of
Social Psychology, 142, 587-600.

Young, A. W., Rowland, D., Calder, A. J., Etcoff, N. L., Seth, A., & Perrett, D. I. (1997). Facial
expression megamix: Tests of dimensional and category accounts of emotion recognition.
Cognition, 63, 271-3 13.

Zalon, M. L. (1993). Nurses' assessment of postoperative patients' pain. Pain, 54, 329-334.









BIOGRAPHICAL SKETCH

Adam T. Hirsh received his B.A. in psychology from the University of Central Florida in

2001. He subsequently enrolled in the doctoral program in Clinical and Health Psychology at the

University of Florida. He was granted an M. S. in 2004, and following completion of a clinical

internship at the VA Puget Sound Health Care System, Seattle, he will graduate with a Ph.D. in

2008. His clinical specialty is in behavioral medicine, and his research interests are in the area of

pamn.





PAGE 1

1 INVESTIGATING PATIENT AND PROVIDER INFLUENCES ON THE ASSESSMENT AND TREATMENT OF PAIN: A NOVEL VIRTUAL PATIENT TECHNOLOGY APPLICATION By ADAM T. HIRSH A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2008

PAGE 2

2 2008 Adam T. Hirsh

PAGE 3

3 ACKNOWLEDGMENTS I thank Michael Robinson for his exceptio nal mentorship throughout my graduate education. He has provided the perfect comb ination of freedom and structure for my development as a scientist. I also thank my dissertation committee R oger Fillingim, Steven George, and William Perlstein for their time and energy. They have each served as models of academic excellence. Special thanks are exte nded to Shankar Manamalkav for his expert technical assistance, and to my colleagues in the Center for Pain Research and Behavioral Health for their friendship and support. I thank the NI H and my Program Director, Linda Porter, for funding this work and my graduate education. Th anks are also extended to the University of Florida and Department of Clin ical and Health Psychology for providing a first-rate training environment. I thank my family for their s upport throughout this and previous endeavors. Finally, I thank my wife Sarah, without wh om none of this would be possible.

PAGE 4

4 TABLE OF CONTENTS page ACKNOWLEDGMENTS...............................................................................................................3 LIST OF TABLES................................................................................................................. ..........6 ABSTRACT....................................................................................................................... ..............7 CHAPTER 1 INTRODUCTION................................................................................................................... .9 Pain Assessment................................................................................................................ .....10 Influence of Sex...............................................................................................................11 Influence of Age..............................................................................................................13 Influence of Race/Ethnicity.............................................................................................15 Pain Treatment................................................................................................................. .......17 Influence of Sex...............................................................................................................18 Influence of Age..............................................................................................................20 Influence of Race/Ethnicity.............................................................................................22 Limitations of the Assessmen t and Treatment Literature.......................................................25 Facial Expression of Pain...................................................................................................... .26 Lens Model Design.............................................................................................................. ...30 Rationale...................................................................................................................... ...........31 2 METHODS........................................................................................................................ .....33 Participants................................................................................................................... ..........33 Measures....................................................................................................................... ..........34 Demographics Questionnaire..........................................................................................34 Gender Role Expectations of Pain...................................................................................34 Lens Model Design.............................................................................................................. ...35 Profiles....................................................................................................................... ......36 Judgments...................................................................................................................... ..37 Procedure...................................................................................................................... ..........38 Hypotheses..................................................................................................................... .........39 Pain Assessment..............................................................................................................39 Treatment with Non-opioid Medication..........................................................................40 Treatment with Opioid Medication.................................................................................40 Recommendations for Change in Medications................................................................41 Healthcare Provider Characteristics................................................................................42 Statistical Analyses........................................................................................................... ......42 Idiographic.................................................................................................................... ...42 Nomothetic..................................................................................................................... .42

PAGE 5

5 3 RESULTS........................................................................................................................ .......44 Participants................................................................................................................... ..........44 Pain Assessment Policies....................................................................................................... .44 Pain Intensity................................................................................................................. ..45 Pain Unpleasantness........................................................................................................46 Mood Assessment Policies.....................................................................................................47 Positive Mood..................................................................................................................47 Negative Mood................................................................................................................47 Treatment Decision Policies...................................................................................................48 Non-opioid Medication...................................................................................................48 Opioid Medication...........................................................................................................49 Recommendation Policies......................................................................................................49 Change in Non-opioid Medication..................................................................................49 Change in Opioid Medication.........................................................................................50 Number of Significant Cues...................................................................................................51 Significance of Contextual Cues............................................................................................51 Number and Significance of Overall Policies........................................................................53 Within-cue Comparisons........................................................................................................54 Pain Assessment..............................................................................................................54 Mood Assessment............................................................................................................56 Treatment Decisions........................................................................................................57 Recommendations...........................................................................................................58 Self-reported Cue Utilization..................................................................................................58 Knowledge of Study Hypotheses and Cue Utilization...........................................................59 Exploratory Group Analyses..................................................................................................60 Participant Characteristics a nd Overall Decision Policies..............................................60 Participant education and overall decision policies.................................................60 Participant professional experien ce and overall decision policies...........................61 Participant Characteristics and Cue Utilization...............................................................61 Participant sex and cue utilization............................................................................62 Participant education and cue utilization..................................................................62 Participant professional expe rience and cue utilization...........................................64 4 DISCUSSION..................................................................................................................... ....65 APPENDIX RESULTS OF IDIOGRAPHI C REGRESSION ANALYSES.............................86 LIST OF REFERENCES.............................................................................................................102 BIOGRAPHICAL SKETCH.......................................................................................................115

PAGE 6

6 LIST OF TABLES Table page 3-1 Demographic and background ch aracteristics of participants...........................................45 3-2 Number of significant cues at each policy.........................................................................52 3-3 Variance in decision policie s explained by contextual cues..............................................53 3-4 Descriptive data on overall policy capturing.....................................................................54 3-5 Means and standard devia tions for ratings within cue.......................................................55 3-6 Number of participants w ith significant overall policies...................................................60 3-7 Participant use of demographic and pain expression cues.................................................63 A-1 Policies toward pain intensity assessment.........................................................................86 A-2 Policies toward pain unpleasantness assessment...............................................................88 A-3 Policies toward positive mood assessment........................................................................90 A-4 Policies toward negative mood assessment.......................................................................92 A-5 Policies toward non-opioid treatment................................................................................94 A-6 Policies toward opioid treatment.......................................................................................96 A-7 Policies toward change in non-opioid treatment................................................................98 A-8 Policies toward change in opioid treatment.....................................................................100

PAGE 7

7 Abstract of Dissertation Pres ented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy INVESTIGATING PATIENT AND PROVIDER INFLUENCES ON THE ASSESSMENT AND TREATMENT OF PAIN: A NOVEL VIRTUAL PATIENT TECHNOLOGY APPLICATION By Adam T. Hirsh August 2008 Chair: Michael E. Robinson Major: Psychology Pain is a misunderstood and mistreated symp tom of acute and chronic illness. Patient demographic characteristics and nonverbal communication displays have been found to influence the assessment and treatment of pain. Numerous methodological limitations of these previous investigations constrain the res earch questions that could be a ddressed and the conclusions that have been yielded. The current analogue st udy employed an innovative research design and novel virtual patient technology to investigate clinical decision making for pain assessment and treatment. Fifty-four currently practicing nurses participated in this study delivered via the Internet. Thirty-two vignettes of virtual patients were presented; each vignette contained a video clip of the patient and clinical summary inform ation describing a post-surgical context. Nurses were asked to make decisions in the following domains: 1) pain intensity and unpleasantness assessment; 2) positive and ne gative mood assessment; 3) nonopioid and opioid medication treatment; and 4) recommendation for a change in non-opioid and opioid me dication. The patient demographic cues of sex, race, and age, as well as facial expression of pain, were systematically manipulated across vignettes and hypothesized to influence assessment and treatment ratings. Idiographic and nomothetic statis tical analyses were conducted to test these hypotheses. Results

PAGE 8

8 indicated that at the id iographic level, patien t demographic and pain expression cues accounted for significant, unique variance in assessm ent and treatment policies among many nurse participants. In several instances, the directi on of the demographic cue effects was unexpected and counter to a priori hypotheses. Patient pain expression was the most prominent cue throughout these policy domains. Within-cue differe nces emerged in the aggregate; the size and consistency of these differences varied acro ss policy domains. Explor atory analyses were suggestive of the role of provi der education, professional expe rience, and practice setting on pain-related decisions. The curr ent investigation demonstrates the application of novel virtual patient technology to the study of pain-related decision-making. Thes e data indicate that patient demographic characteristics and f acial expressions of pain often play a significant role in the assessment and treatment of acute post-surgical pa in. Implications of the present findings are discussed in the context of th e extant literature. Methodologi cal considerations and future research directions are also discussed.

PAGE 9

9 CHAPTER 1 INTRODUCTION Despite recent increases of pain management c ontent in the literature (Ferrell et al., 1993), the development of specific pain curricula for several pain-related health disciplines (International Association for the Study of Pain [IASP], 1993, 1997), and the availability of clinical practice guidel ines (World Health Organization [WHO], 1986; American Pain Society [APS], 1992; Acute Pain Management Guidelin e Panel, 1992), pain remains a misunderstood and mistreated symptom of acute and chronic illne ss. Previous research has estimated that more than 80% of pain sufferers receive insuffici ent pain relief, largely due to excessively conservative pharmacologic treatment (WHO, 19 86). Because pharmacotherapy with analgesic medications is one of the primary foundati ons of pain management, overly conservative approaches may deny adequate pain relief to increasi ng numbers of patients. Poor pain management due to insufficient admi nistration of analgesic medications is likely the result of several in teracting factors (Portenoy, 1996). Many of these factors are the product of inadequate knowledge and inappropr iate attitudes on the part of h ealth care providers about pain in general, and pain assessment and treatment in particular. In compensating for knowledge deficits concerning pain assessment and pha rmacologic pain management, providers may wittingly or unwittingly permit thei r own biases to exert undue in fluence over clinical decisionmaking. Consequently, less knowledgeable providers may mismanage pain and, thus, needlessly prolong the suffering of patients through the implementation of uns ystematic clinical decision policies. Thorough investigations of those factors that influen ce the clinical decisions of providers regarding pain assessment and the administration of analgesic medications is, therefore, necessary to improve the care of patients in pain.

PAGE 10

10 Pain Assessment An individuals assessment of the pain expe rience of another pers on is likely based on many factors. In the clinical context, medical and disease related variables of the observed patient are of clear importance. Additionally, char acteristics of the observer, such as his/her beliefs regarding appropriate pain behaviors and stereotypical soci al and gender roles, as well as his/her acuity in observing overt behaviors, ar e hypothesized to have a large impact on the assessment of pain in others. In addition, qualitie s of the person who is being observed, such as sex, age, and ethnicity, must also be consider ed (Teske, Daut, & Cleeland, 1983). The observers perception of the pain experien ce of another, then, most likel y results from an interaction between characteristics of the observer and the person that individual is observing. In the clinical literature, there has been a c onsiderable amount of attention paid to nurses estimation of patients pain. Much of this li terature has focused on pain assessment accuracy, with mixed results. A frequently cited study by Zalon (1993) found that nurses visual analogue scale (VAS) pain ratings were significantly, yet modestly, correlated with the pain ratings of postoperative abdominal surgery pa tients. Interestingly, these nur ses over-estimated mild pain and under-estimated more severe pain. Patients pain was the only significant factor accounting for approximately 9% of the variance in nurses assessments that was related to the accuracy of nurses pain assessments. These findings parallel those of other studie s, revealing small but significant correlations between nur ses pain assessments and pa tients self reports, and the tendency for nurses to mises timate patients pain (Choinier e, Melzack, Girard, Rondequ, & Paquin, 1990). Similarly, Salmon and Manyande (1996) found that nurses frequently underestimate patients need for analgesia and thei r ability to cope with pain. Others, however, have found little to no agreement between the pain ratings of nurses and patients, presumably because these nurses relied exclusively on obs erved behavior (Thomas, Robinson, Champion,

PAGE 11

11 McKell, & Pell, 1998). This body of literature suggests that hea lth care providers often make inaccurate judgments regarding the level of patients pain. The concerning implication is that these misestimates influence the decisions that providers make regarding medication administration for pain. Influence of Sex As noted above, sex of both the observer and obs erved, in addition to the observers beliefs about sex and gender, may influence the pain assessment process. There has been a recent increased interest in sex and ge nder differences in pain. Research has shown that there is a discrepancy between the relatively small sex differ ences in clinical pain report and the moderateto-large differences in experimental pain re port. Robinson and collea gues developed the Gender Role Expectations of Pain (GREP) questionnaire to explore the hypothesis that the differences in experimental pain are an artifact of the laborat ory setting where gender ro les are activated. Their research has shown that males and females report significant differences in their pain expectations for self and othe rs (Robinson et al., 2001). Importa ntly, these expectations are associated with experimental pa in responding, and have been shown to explain more variance in pain reports than sex (Wise, Price, Myers, Heft, & Robinson, 2002). Subsequent research focused on the assessment of pain in others. Specifically, this rese arch was conducted to determine if males and females perceive pain a nd pain-related emotions in others differently based on the sex of the individual (Robinson & Wise, 2003). This study involved participants viewing videotaped recordings of others undergoing a cold pressor task, after which they provided ratings of perceived pain experience of the videotaped participant. Results indicated that (1) viewers rated male vide os as having less pain than fema le videos; (2) for both male and female videos, female viewers ra ted observed pain intensity highe r than did male viewers; (3) both male and female video participants pa in was underestimated, but males pain was

PAGE 12

12 underestimated more than females pain; (4) expe ctations of gender-rela ted endurance of pain significantly predicted ra ting of both male and female videos ; (5) when endurance expectations were controlled, sex of the viewer no longer significantly predicted observed pain ratings. The data from these studies suggest that not only do gender stereotypes influence ones own pain ratings and pain behavior, but they also influence ones perceptions of the pain experience of others. This research was c onducted in the laborato ry and investigated experimental pain. As such, these findings cannot directly address the clinically relevant issues of whether gender role expectati ons influence pain assessment a nd decisions about treatment and prescription practices for pain in the medical setting. These fi ndings do, however, underscore the need for such research, which has heretofore no t been conducted. The few investigations that have been conducted on the influe nce of patient sex on clinical pa in assessments are mixed. In an early study of cancer patients, a gr eater patient-provider discrepanc y about the perceived severity of the patients pain was found for females (Cl eeland et al., 1994). A later study of 281 minority cancer patients found no sex differences in the proportion of males and females whose pain was underestimated by their physicians (Cleeland, G onin, Baez, Loehrer, & Pandya, 1997). In this study, underestimation was high for both male and female patients (66% and 72%, respectively). In a more recent study of minority cancer patie nts, Anderson and colleagues (2000) did find evidence of a sex disparity in pain assessment. Consistent with result s from Cleeland et al. (1994), they found that physicians underestimated the pain severi ty of 79% of female patients compared with 59% of male patients. The important contribution of these investig ations to the pain assessment literature notwithstanding, the genera lizability of their findings may be constrained for several reasons. First, all were investigations of cancer patients. The implications and appraisals of cancer-related

PAGE 13

13 pain may be very different from non-malignant pain conditions, given that cancer is a lifethreatening illness. This may impact on both the patient and provider assessment of pain. Second, the racial/ethnic status of the patients in these studies may have confounded the results. Both the Cleeland et al. (1997) and Anderson et al. (2000) studies incl uded only patients of racial/ethnic minority status; th e Cleeland et al. (1994) study di d not report the racial/ethnic characteristics of their sample. Given that minor ity individuals are at increased risk of having their pain under-assessed and under-treated (see be low), this fact may co mplicate interpretations of these investigations. For example, the null fi nding of the Cleeland et al. (1997) study may be due to the overwhelming ubiquity of pain underestimation in these minority patients, which may have obscured any sex difference in pain assessme nt. Finally, the vast majority of providers in these studies were Caucasian male physicians. Since patient-provider demographic congruence may influence the medical encounter (Anderson et al., 2003), and the number of providers from diverse racial/ethnic and sex b ackgrounds is increasing (American Medical Association [AMA], 2005; Girard, 2003), future investiga tions that include a diverse ra nge of providers are certainly warranted. Influence of Age The experience of pain is common in older populations, with preval ence estimates ranging from 45-80% depending on the residential status of the sample (Fries, Simon, Morris, Flodstrom, & Bookstein, 2001; Herr, 2002; Mobily, Herr, Clark, & Wallace, 1994; Teno, Weitzen, Wetle, & Mor, 2001; Weiner, Peterson, & Keefe, 1999; Werner, Cohen-Mansfield, Watson, & Pasis, 1998). Despite these estimates, pain assessment in the elderly is poor relative to younger populations (Ferrell, 1996; Gloth, 20 00; Horgas & Elliott, 2004). The assessment of pain in older populations is complicated by several factors. Ol der adults may hold the belief that pain is a normal and expected part of aging and, thus fail to adequately communicate their pain

PAGE 14

14 experience to others (Ferrell, 1995). Indeed, older adults tend to under-report pain relative to younger populations (Bellville, Forrest, Miller, & Brown, 1971; Oberle, Paul, Wry, & Grace, 1990). Since self-report is the gold standard of clinical pain asse ssment, this tendency increases the likelihood of a sub-optimal outcome of the pa in assessment process in the elderly patient. The self-report of pain is further complicated in older adults by the paucity of adequately standardized assessment instruments for th is population. The psychometric properties of instruments that have been thoroughly standard ized in younger patients [e.g., VASs, McGill Pain Questionnaire (MPQ; Melzack, 1975)] are often compromised when employed in the elderly (Gagliese, 2001). Higher rates of medical comorbidities in the elderly may also impact on the assessment of pain in these patients, as such comorbiditie s may compete for the attention of healthcare providers (Nishikawa & Ferrell, 1993). Among th e possible comorbidities, dementia is of particular importance. Not only does dementia ha ve the likelihood of comp eting for the attention of providers, but, perhaps more importantly, it introduces a leve l of complexity to the pain assessment process that is unmatched by other cond itions. As the severity of dementia increases, the level of self-awareness and ability to co mmunicate decreases (White & Cummings, 1997). The pain assessment process is consequently compromised. Evidence for this is found in the substantial literature documenting the substandard assessment of pain in elderly patients with cognitive dysfunction (Cohen-Mansfield & Lipson, 2002a, 2002b; Cook, Niven, & Downs, 1999; Kaasalainen et al., 1998; Ferre ll, 1995; Sengstaken & King, 1993). A final complication of the assessment of pain in older adults concer ns the attitudes and beliefs of providers. Research suggests that prov iders may be overly cautious in considering the pharmacologic management of pain in genera l (Portenoy, 1996), and this approach may be

PAGE 15

15 heightened in the elderly (Aubrun, 2005). This cautiousness is likely to impact the pain assessment process, since this process is unde rtaken for the primary purpose of informing treatment decisions. Providers may also hold belief s similar to the elderly themselves concerning the normative experience of pain in this population and/or the decreased pain sensitivity of older adults (Sarkisian, Hays, Berry, & Mangione, 2001). These beliefs may, in turn, inappropriately influence the pain assessment of such patients. It is unlikely that the factors noted above operate in isolati on. Rather, they are likely to interact with each other and ot her variables to influence the assessment of pain in elderly patients. Although the extent of this interaction has not been entire ly elucidated, it is clear that elderly individuals are at increased risk of sub-optimal pain assessment and, consequently, pain management. As such, continued work in this area appears warranted. Influence of Race/Ethnicity There has been a recent surge of investiga tions on race/ethnic differences in pain experience. In the experimental context, Afri can-Americans and Hispanics reliably demonstrate lower pain tolerance and higher pain unpleasantness than Whites across a range of pain stimuli (Campbell, Edwards, & Fillingim, 2005; Ed wards & Fillingim, 1999; Walsh, Schoenfeld, Ramamurth, & Hoffman, 1989). Race/ethnic differe nces in pain perception have also been reported for several chronic pain conditions, in cluding AIDS (Breitbart et al., 1996); glaucoma (Sherwood et al., 1998); migraine (Stewart, Lipton, & Liberm an, 1996); arthritis (Creamer, Lethbridge-Cejku, & Hochberg, 1999); postopera tive pain (Faucett, Gordon, & Levine, 1994); and myofascial pain (Nelson, Novy, Averill, & Berry, 1996). There is also an expanding literature concerning race/ethnic disparities in th e treatment of pain, which is reviewed below. Differences in the pain assessment process have been investigated as one possible explanation for these treatment disparities. The results of these studies are mi xed. On the one hand,

PAGE 16

16 race/ethnic differences have been reported in the ability of hea lth care providers to accurately interpret patient pain. For example, results of a large multi-center study indicated that minority cancer patients were more likely to have the severity of their pain underestimated by their physicians than White patients (Cleeland et al ., 1997). Sheiner and coll eagues (1999) found that although Jewish and Bedouin parturients gave sim ilar self-reports of pain, the medical staff consisting entirely of Jewish providers per ceived Bedouin women as experiencing less pain than Jewish women. Although thei r study included only minority patients with cancer and, thus, did not allow for comparisons to Caucasian pa tients, Anderson and coll eagues (2000) found that physicians underestimated the pa in severity of 74% and 64% of African-American and Hispanic patients, respectively. In addition to being at increased risk of having their pain misestimated, there is evidence that minority patients may also be less likely to have their pain documented altogether (Berna bei et al., 1998). A number of possible explanations for these di sparities exist. Race/ethnic differences in language and communication, socioeconomic status, access to healthcare, symptom manifestation, and patient preference may all play a role in this context. Provider bias must also be considered. It is important to note, however, that such disparities in pain assessment are not always found. For example, Todd, Lee, and Ho ffman (1994) investigated the concordance between patient and provider pain assessments in the Emergency Department. Caucasian and Hispanic patients presenting with isolated ex tremity fracture were included in this study. Although physician estimates of pain were lower th an patient reports, there was no difference in physician estimates of pain between the groups. Fu rthermore, in contrast to the finding noted above that minority patients may be less likely to have their pain reco rded (Bernabei et al., 1998), Todd, Deaton, DAdamo, and Goe (2000) found no difference in the frequency of pain

PAGE 17

17 documentation between Caucasian and African-Ame rican patients presenting to the Emergency Department. An important ambiguity of many of these stud ies is whether the difference if one is found in provider estimation of pa tient pain is the result of fact ors within the medical staff who rated the pain (e.g., knowledge de ficits, bias), or factors rela ted to the patient groups (e.g., communication, SES). Future inve stigations are needed to el ucidate these issues. Such investigations have the potential of improving the pain assessment process and, consequently, treatment of all patients in pain. Pain Treatment Optimal treatment of pain is an important goa l of healthcare. Compli cations of unrelieved pain are widespread and varie d. Physical, functional, and psyc hologic conditions are associated with and exacerbated by pain. These include depression, sleep distur bance, and functional impairment (Ferrell, 1995; Herr & Mobily, 1991; Lavsky-Shulan et al., 1985; Parmalee, Katz, & Lawton, 1991; Williamson & Schulz, 1992). There is also evidence of increased morbidity and mortality secondary to poor pain control (C ousins, 1991). Despite recent increases of pain management content in the lite rature (Ferrell, McGuire, & Do navan, 1993), the development of specific pain curricula for se veral pain-related health disciplines (IASP, 1993, 1997), and the availability of clinical pr actice guidelines (WHO, 1986; APS, 1992; Acute Pain Management Guideline Panel, 1992), pain remains a misunde rstood and mistreated symptom of acute and chronic illness. Approximately 23 million Americans experience pos t-operative pain each year, and as many as 75% do not achieve adequate pain control despite the availability of effective treatments (Cousins, 1994). In te rms of chronic pain, the difficu lties of managing this condition are axiomatic. Of the many possible contributing factors that may account for the overwhelming

PAGE 18

18 evidence of poor pain management practice, pati ent demographic characteristics have been the target of recent empirical focus. Influence of Sex The accumulation of evidence from both the labo ratory (e.g., the influence of gender role stereotypes on pain assessments) and the clinical setting (e.g., the ina ccuracy of health care providers pain assessments of patients) suggest s that an individuals sex may influence the assessment of pain in others and, thus, may elicit differential treatment practices from health care providers. Research investigating clinicians beli efs regarding sex differen ces in pain perception is equivocal. Some studies indicat e that many health care providers believe differences in pain perception between males and females do exist (McC affrey & Ferrell, 1992), while others reveal no such belief among providers (Holm, Cohen, Dudas, Medema, & Allen, 1989). In terms of actual pain management practice, th e literature is similarly conflictual. Results of many investigations indicate that females are at increased risk of having their pain undertreated relative to males. Retrospective studi es have demonstrated a tendency for males to receive a higher frequency of narcotic analgesics (Calderone, 1990) and la rger initial doses of pain medication (McDonald, 1994) post surgery. Th is sex disparity in post-operative pain management has also been found in pediatric and elderly samples. In one study, men and boys were prescribed significantly more narcotic medication following cardiothoracic surgery than similar females (Beyer, DeGood, Ashley, & Russ ell, 1983). Faherty a nd Grier (1984) reported that medical providers prescribed significantly less pain medication for adult females of all age groups, including the elderly, following abdominal surgery compared to males. Female cancer patients with pain have also been found to be un der-medicated relative to males (Cleeland et al., 1994). In more experimental designs, research em ploying identical vignettes, save the sex of the

PAGE 19

19 patient, has shown that nurses c hoose less analgesic medications to be administered to females than males (Cohen, 1980; McDonald & Bridge, 1991). Evidence that patient sex does not influence pain management practices has also been reported. Bartfield and colleague s (1997) conducted a prospective study on adult patients with acute low back pain presenting to the Emergenc y Department. Patient sex did not emerge as a significant predictor of analgesic administration in this sample. Similar findings were reported by Turk and Okifuji (1997) in a heterogeneous samp le of chronic pain patients. A later study by Turk and Okifuji also found no significant sex differe nces in current use of analgesics or past treatment in a large sample of chronic pain and cancer-related pain patients (Turk & Okifuji, 1999). Campbell (2002) employed a vignette desi gn to the study of nurses decision-making regarding pain management practices. Results of this study indicated that the vast majority of nurses were not predisposed to administer le ss opioid medication to hypothetical female postsurgical patients. Also noteworthy is the fact that the earliest investigations of sex differences in pain management practice found that males, not fema les, were at increased risk of receiving suboptimal analgesic care (Bond & Pilowsky, 1966; Pilowsky & Bond, 1969), perhaps due to a culturally sanctioned belief that males should be more tolerant of pain than females (Bond, 1971). These disparate findings question the reliability of sex as an influence on the prescription of pain medication. Furthermore, as Robinson a nd Wise (2003) note, any conclusions that are drawn from this body of research should be tentative due to methodological issues. Two additional issues of concern with the entirety of the clinical liter ature that cannot be overstated are (1) the absence of any significant manipul ation of the independe nt variables in an ecologically valid manner, and (2) the noted lack of variability in the sex of the assessor (e.g.,

PAGE 20

20 medical provider). Clearly, then, additional research is needed to clarify the issue of sex and pharmacologic pain management. Influence of Age As noted above, elderly individu als manifest high rates of bo th acute (e.g., post-surgical) and chronic (e.g., arthritis) pain conditions. Although there appears to be some evidence of agerelated changes in pain percep tion, such as declines in endoge nous analgesic systems (Edwards, Fillingim, & Ness, 2003) and altered transmissi on along A-delta and C nerve fibers (Chakour, Gibson, Bradbeer, & Helme, 1996; Helme & Gibs on, 1997), the effects of these changes on the experience of pain remain unclear. Results of la boratory-based studies co mparing pain threshold and tolerance across age groups are mixed. This has led some to question the clinical significance of age-related ch anges in pain perception (F errell, 2003; Harkins, 1996). Despite the aforementioned lack of empirical consistency, clinical practice guidelines for pain management assert that elderly patients are at risk of being under-tre ated for pain (Agency for Health Care Policy and Research [AHCPR], 1994). As previously noted, the consequences of uncontrolled pain are considerab le. These consequences are he ightened in the older adult population (American Geriatrics Society, 2002). The elderly under go surgery four times more often than other age groups (Rooke, Reves, & Rosow, 2002), and greater than half report inadequate post-operative pain relief (Karani & Meier, 2004). Results of a study by Oberle and colleagues (1990) indicated that, compared to y ounger patients with similar reports of pain intensity, elderly patients received smaller amount s of analgesics following surgery. Results of a vignette study also indicated that at least some health care providers use age as a significant cue in the decision making process regarding use of pain medication, such that some nurses were predisposed to administer less medication to older patients (Campbell, 2002). These findings hold increased significance when considered in li ght of empirical eviden ce that older patients do

PAGE 21

21 not self-administer fewer analgesics than ot her patient groups (Mor gan & Puder, 1989; Owen, Szekeley, Plummer, Cushnie, & Mather, 1989) ; although, contradictory findings have been reported (Gagliese, Jackson, Ritvo, Wowk, & Katz 2000; Gagliese & Katz, 2003). The literature concerning management of chronic pain in elderl y individuals is relativ ely small. The use of opioid drugs for chronic, non-mali gnant pain is controversial in general, but they are likely underutilized in treatment of the elderly (Popp & Portenoy, 1996). The literature provides some support for this contention (AGS, 2002; Auret & Schug, 2005; Ferrell, Ferrell, & Rivera, 1995). Several possible explanations for these resu lts are available. Age-related physiologic changes in response to opioids (e.g., increased risk of organ toxicity, increased sensitivity to medication effects) may induce caution in heal th care providers in administering such medications to older individuals in pain. Although these changes certainly complicate the use of analgesics in these patients, the application of ageist stereotype s to the decision making process which the literature suggests ex ists to some degree is not su pported. As reviewed earlier, pain assessment in elderly patients may be compli cated by many factors (e.g., cognitive impairment, provider attitudes), which has clear implications for pain management. Not only can cognitive impairment negatively affect pain assessmen t but it can also be aggravated by both postoperative pain and the medications used to trea t this pain (Montamat, Cusack, & Vestal, 1989; Moore & OKeefe, 1999). Effects of these complica tions are seen in the research demonstrating that cognitive impairment strong ly influences the amount of an algesic medication that medical providers administer to older patients following trauma or in the post-operative period (Foster, Pardiwala, & Calthorpe, 2000; Feldt, Ryden, & M iles, 1998; Morrison et al., 2003), as well as in the nursing home environm ent (Horgas & Tsai, 1998).

PAGE 22

22 Influence of Race/Ethnicity In contrast to the relatively small and inconsistent literature concerning race/ethnic influences on the pain assessment process, th ere is considerable empirical support for the assertion that race/ethnicity play s an important role in the trea tment of acute and chronic pain. Race/ethnic disparities in pain management ha ve been reported across a range of conditions. Racial/ethnic disparities in Emer gency Department (ED) pain mana gement have been reported in several studies. In a series of retrospective studies, Todd a nd colleagues (1993, 2000) found that Hispanic and African-American patients were more likely than Whites to receive no pain medication upon admission to the ED with isolat ed long bone fractures; these disparities were not due to ethnic differences in physician pain assessment (Todd et al., 1994). More recently, Tamayo-Sarver, Hinze, Cydulka, and Baker (20 03b) found that African-American migraine and back pain ED patients were less likely to be prescribed opioids than similar White patients. Importantly, this disparity was greatest for conditions with fewer objective findings (e.g., migraine), which presumably permitted non-medical factors, such as ra ce/ethnicity, to play a larger role in medi cal decision-making. Management of post-operative and back pain also evinces racial/ethnic disparities. A retrospective analysis of post-su rgical pain management practices indicated that White patients consistently received higher dos es of analgesics than African-A merican and Hispanic patients; these differences persisted after controlling for relevant demogra phic and clinical variables (Ng, Dimsdale, Shragg, & Deutsch, 1996b). A follow-up investigation by these same researchers found that White patients were prescribed a la rger amount of patient-controlled analgesia for post-operative pain than Hispanic patients, a nd African-American patients were prescribed a larger amount than Hispanic and Asian pati ents (Ng, Dimsdale, Rollnik, & Shapiro, 1996a). Again, these disparities remained significant after controlling for potential confounds.

PAGE 23

23 Racial/ethnic disparities in the management of cancer and HIV/AIDS-related pain have also been documented. Cleeland and collea gues (1994), in a multicenter study, found that outpatients of cancer clinics that primarily serv e ethnic and racial minorities were three times more likely to be undermedicated with analgesi cs than were patients in other settings. The percentage of patients indicating inadequate analgesia wa s significantly higher in community clinical oncology programs that treated predomin antly African-American and Hispanic patients than in academic medical cancer centers a nd community-based hospitals and practices. Furthermore, African-American and Hispanic patients were more likely than non-minority patients to receive inadequate pa in management in all settings. In a subsequent investigation, these researchers found that patie nts treated in settings that primarily serviced AfricanAmericans, Hispanics, or both we re more likely to receive inad equate analgesia than patients treated in non-minority community treatment se ttings (Cleeland et al., 1997). Bernabei and colleagues (1998) found that (1) elderly African-American and Hi spanic cancer patients were less likely to have their pain recorded compar ed to Whites, and (2) mi nority nursing home cancer patients were more likely to have received no an algesia. Specifically, African-Americans had a 63% increased probability of having their pain untreated compared to White patients. Similar disparities were observed for other racial and ethnic groups, although small sample sizes precluded detailed analyses of these disparities. Similar results were reported by the Office of Minority Health; 62% of cancer patients at medica l facilities serving primarily African-American patients and 82% of cancer patients at medical facilities serving primarily Hispanic patients were prescribed inadequate analgesic medication (R oss, 2000). Less dramatic but still noteworthy, Anderson and colleagues (2000) found that approx imately one-third of African-American cancer patients received pain medications of insufficient strength to adequately ma nage their pain. In the

PAGE 24

24 HIV/AIDS literature, differences in pain treatme nt have been found between members of racial minority groups and Whites, with minority patien ts receiving less adequate pain management (Sambamoorthi et al., 2000). Although the evidence for racial /ethnic disparities in the treatment of pain appears overwhelming, contradictory findings have be en reported. Karpman, Del Mar, & Bay (1996) attempted to replicate the findi ngs of Todd et al. (1993) and determine the existence of a relationship between patient race/ ethnicity and the amount of anal gesia administered to reduce pain from a long bone fracture. In contrast to the earlier study by Todd et al. (1993), no differences between Hispanic and White patient s were found in terms of pharmacologic pain management practice for fracture reduction. A mo re recent retrospectiv e study also sought to investigate the influence of patient race/ethni city on decision making for pain management following bone fracture (Fuentes, Kohn, & Neig hbor, 2002). Consistent with Karpman and colleagues (1996), no differences in analgesic tr eatment practices for White, African-American, Hispanic, or Asian patients were noted. Bartfi eld and colleagues (1997) conducted a prospective study of adult patients treated for non-traumatic low back pain to determine the influence of physicians impression of patients race/ethnicity on analgesic pr escription practices. Results indicated that only patient pain, not race/ethnic ity, influenced analgesic administration. Two recent vignette studies also found that race/ethni city of hypothetical patie nts did not influence analgesic practice among physicians and nurses (Campbell, 2002; Tamayo-Sarver et al., 2003a). When considered in its entirety, the literatur e indicates that patient race/ethnicity is an important variable in the treatment of pain. A lthough not perfectly consistent, this literature demonstrates that African-Americans and Hispanic s are more likely to be under-treated for pain than their White counterparts. Furthermore, su ch disparities were found in diverse medical

PAGE 25

25 facilities and geographic locations. These disparities may result from many factors, including communication difficulties, diffe rential expression and manife station of pain, differential preferences and expectations for treatment, a nd frank provider racism. Continued effort to elucidate treatment disparities in pain manage ment practices and the reasons behind them is needed to improve the treatment of pain in all people. Limitations of the Assessmen t and Treatment Literature Although the literature regardi ng the issues outlined above has expanded and improved in recent years, conspicuous limitations and gaps remain. A primary issue is methodological. The two principal approaches to st udying pain assessment and treatme nt are the retrospective and vignette designs. Retrospective designs have genera lly taken the form of chart reviews in which patient medical records are review ed to determine if patient dem ographic factors are related to medical assessment and/or treatment. These designs are fraught with problems that make it difficult to test hypotheses a nd draw firm conclusions. Sp ecifically, they preclude any manipulation of the independent variables of interest (e.g., patient demographics) and limit the analysis of potential confounds. Additionally, pa in report is often not documented in patient charts (Calderone, 1990; McDona ld, 1994; Ng et al., 1996), placi ng further constraints on the applicability of retrosp ective designs to pain research in this context. A more methodologically sound approach is the vignette study. These designs typically involve the reading of a hypothetical patients file by the medical provider, after which th e provider answers a series of questions regarding the patients pain level a nd the medication administration they (provider) would endorse. Although these designs permit greater control in the manipulation of variables, they suffer from low external validity and hi gh task transparency/s ocial desirability. The lack of variability in the demographic char acteristics of the health care provider is an additional limitation. As previously noted, the me dical field has seen an expansion of both sex

PAGE 26

26 and racial/ethnic diversity among providers (AMA, 2005; Girard, 2003); however, the vast majority of participants in research investiga tions to date have been Caucasian and of the stereotypic sex (i.e., male physicians and fe male nurses). Not only may patient-provider congruence on these characteristics influence th e medical encounter (And erson et al., 2003), but demographically diverse providers may appro ach clinical problems (e.g., pain) and their assessment and management in systematically diffe rent ways due to the influence of culturallysanctioned attitudes and beha viors regarding health. The literature to date, both in the labo ratory and the medical setting, has soundly demonstrated the importance of investigating the factors that influence medical assessments and decisions, especially regarding patient pain and analgesic medication administration. However, a creative, more methodologically sound research de sign is now needed to probe those questions that remain unanswered and re-evaluate the conc lusions that have been drawn thus far. The proposed study aims to take this next logical step in the literature regard ing pain assessment and management by health care providers. Facial Expression of Pain Patient self-report is the gold st andard of pain assessment and typically takes the form of a verbal response to a pain-related inquiry by the health care provider Self-report of pain may also be obtained through response to items from a que stionnaire and/or one of many rating scales (e.g., VAS, NRS). Nonverbal expressi ons of pain offer a promising adjunct to these self-report indices (Craig & Prkachin, 1983) and are likely to impact on the pain assessment and treatment process. In fact, observers generally assign greate r weight to nonverbal ex pressions of pain than self-report (Craig, 1992; Pool e & Craig, 1992). Among the many variants of nonverbal pain behaviors, facial expressions have been the su bject of much empirical work. The foundation of this work is largely provided by Paul Ekman and his colleagues who have demonstrated the

PAGE 27

27 existence of distinct facial expressions repr esenting fundamental emotional states (Ekman, 1992). These states can be accurately detected by observers on the basis of specific facial cues (Ekman & Friesen 1969b; Ekman, Friesen, & Ellsworth, 1983). Kenneth Craig and his colleagues extended this line of research to the fiel d of pain in the 1980s, which is not to say that facial expressions of pain were ignored until only recently. In fact, Darwin (1872/1965) commented on specific mouth and eye movements that he considered charac teristic of the human expression of pain. Although these facial features did not hold up to late r empirical scrutiny, the notion that the facial expression of pain could be quantified by analysis of specific movements of facial muscles portended a field of inquiry that did not come to fruition until a full century later. This field of inquiry has been aided in la rge part by development of technologies capable of capturing specific morphological features of distinct facial expressions. The Facial Action Coding System (FACS; Ekman & Friesen, 1978) is the primary such technology. The FACS is an objective, anatomically-based system that permits a full description of the basic units of facial movement associated with private experience, including pain. Forty-four different action units (AUs) scored on a 5-point intensity scale have be en identified, which represent the minimal units of facial activity that are anatomically separate and visually distinguis hable. Core action units representing the facial expression of pain in adults are: brow lowered, cheek raised and lid tightened, nose wrinkled and upper lip raised, and eye closure (Craig, Prkachin, & Grunau, 1992; Prkachin, 1992b). Although the asso ciated changes in facial musc ulature for pain and other expressions occur along continuous dimensi ons, these expressions are perceived in a categorical manner (Etcoff & Magee, 1992; Young et al., 1997). Importantly, the pain expression is relatively specific to pain, as it can be differentiated from othe r negative subjective states, such

PAGE 28

28 as disgust, fear, anger and sadness (LeResch e, 1982; LeResche & Dworkin, 1988; Hale & Hadjistavropoulos, 1997). Following identification of the characteristic faci al expression of pain, scientists turned to investigations of the developm ental and cultural stability of this expression, as well as to investigations in diverse experimental and clinic al contexts. A specific f acial expression of pain appears to be present from an early age. Infa nts from 25 weeks gestati on show a characteristic pain face (Craig, Whitfield, Grunau, Linton, & Hadjistavropoulos, 1993; Grunau & Craig, 1987; Lilley, Craig, & Grunau, 1996; Stevens, Johnston, & Horton, 1994). Strong consistencies in the morphology of facial expressions of pain have been observed from birth through old age; however it is important to note that these expres sions are subject to e nvironmental pressures, particularly those related to so ciocultural norms and immediate c ontext (Craig, 1980). In contrast to the sizeable literature on developmental aspect s of the pain expression, little cross-cultural investigations have been conducted. This is a conspicuous gap in the literature given that pain behaviors may vary dramatically both between a nd within cultures (Goldberg & Remy-St. Louis, 1998). Cross-cultural studies of facial expression of emotions have been conducted (Ekman & Friesen, 1971; Ekman et al., 1987); however, the exte nt to which these studies generalize to pain is not clear. Unfortunately, this is a diffi cult area of inquiry due to methodological and interpretation constraints (Ekman, 1994; Fr idlund, 1994; Haidt & Ke ltner, 1999; Russell, 1994, 1995), but one that is in need of development. The facial expression of pain across different experimental pain stimuli (Prkachin, 1992b) and clinical pain conditions (Craig, Hyde, & Patrick, 1991; Ha djistavropoulos & Craig, 1994; LeResche, 1982; LeResche & Dworkin, 1988; Prk achin & Mercer, 1989) has been investigated and appears to be relatively constant. Furthermor e, the magnitude of facial expression has been

PAGE 29

29 shown to increase in relation to exacerbations of clinical pain intensity and to be related to several indices of clinical pain severity (C raig et al., 1991; Hadjistavropoulos, LaChapelle, Hadjistavropoulos, Green, & Asmundson, 2002; Le Resche & Dworkin, 1988; Prkachin, Berzins, & Mercer, 1994; Prkachin & Mercer, 1989). The complement to a distinctive facial expression of pain is the ability of others to detect it. As noted above, observers can re liably distinguish the facial expr ession of pain from that of other subjective states. Research in dicates that the facial cues inhe rent in the expression of pain are used consistently by observers to judge pain in adults and children (Craig et al., 1991; WattWatson, Evernden, & Lawson, 1990); however, the accur acy of these judgments is inconsistent (see below). Facial expressions of pain make substantial contri butions to observer ratings of others pain (Ahles et al., 1990; Hale & Hadjistavropoulos, 1997) even when a contradictory verbal report of the absence of pain is pr esented (Poole & Craig, 1992) Caregivers of the profoundly cognitively impai red have been noted to rely heav ily on facial expressions as an index of experienced pain (LaChapelle, Hadjistavropoulos, & Craig, 1999; van Dongen, AbuSaad, & Hamers, 1999). The overall literature concer ning observer accuracy of pain estimation based on facial expressiveness is relatively small, particularly when health care providers estimations are the target of investigation. Laypersons and providers are generally accurate in judging the presence or absence of pain based on f acial expressions of the observe d person (Breau et al., 2001; Goodenough et al., 1997; Lilley et al., 1996; Lindh, Wiklund, Sandman, & Hakansson, 1997; Prkachin et al., 1994). However, when judgments involve pain severity and not merely the presence or absence of pain, c oncordance between observer and obs erved pain ratings decreases. For example, Prkachin and colleagues (1994) vi deotaped the faces of patients experiencing

PAGE 30

30 shoulder pain and found that untrained observers of these tapes underestimated patients pain by as much as 80%. In a more recent study, Pr kachin, Solomon, Hwang, and Mercer (2001) investigated the influence of pain familiarity on accuracy of pain judgments. Three groups of participants (laypersons with a family history of pain condi tions, laypersons with no family history of pain conditions, and health care provide rs) viewed videotapes of patients undergoing a painful medical procedure, after which they pr ovided pain assessments of these patients. Patients pain was underestimated by each group of participants, but to varying degrees. Participants with a family history of pain attri buted greater pain to the patients than did those with no such history. Furthermore, health care providers (physical and occupational therapists) attributed the least amount of pa in to patients. These data sugg est that personal and professional experience with pain may influence the assessm ent of pain in others. In a study by Goodenough and colleagues (1997), pediatric nurs es indicated that they relied heavily on facial cues when making global estimates of pain. However, resu lts revealed a significan t discrepancy between childrens self-report of pa in (visual analogue toy) and the nu rses judgment, such that nurses ratings of patient pain were consistently lower than the patients ratings Although this literature is small, it is generally consistent with research reviewed earlier in which provider underestimation of patient pain appears to be the rule rather than the exception. Lens Model Design As noted above, methodological shortcomings ar e characteristic of the extant literature concerning pain assessment and treatment. The proposed study will attempt to address these shortcomings by employing a lens model desi gn. The lens model is an analogue method for capturing how individuals use information in their environment to form judgments. It is both a theoretical model of how individuals use information to ma ke judgments, and an experimental paradigm for studying judgment processes and outcomes (Beal, Gillis, & Stewart, 1978). The

PAGE 31

31 lens model was originally formulated by E gon Brunswick in the 1950s and later refined by Kenneth Hammond within his Social Judgment Theory (Cooksey, 1996). Inherent in the lens model approach is the assumption that ju dgment processes are contextually determined. That is, an individua ls judgment is determined based on his/her attention to and weighting of th e information (cues) available in the immediate environment. In lens model applications, individual s are presented a series of prof iles containing cues that may be used to form a judgment. The profiles depict cases or situational contexts for the individual to process, and each contains a unique combination of cues. The outcome of the judgment process for each profile is obtained using a quantifiable response mode, such as NRSs or VASs. Policy capturing occurs at the idiographic level util izing multiple regression procedures. A linear equation is produced that optimally weights each cu e in terms of its predictive contribution to the judgments. Once an individuals judgment policy has been captured, a coefficient of multiple determination (R) can be generated, which re presents the proporti on of the variance in judgments accounted for by the linear model of the individual. This model also permits data aggregation for group analyses. Rationale As reviewed above, there are varying degrees of evidence that an individuals demographic characteristics namely, sex, age, and race in fluence observers assessment and treatment (in the case of medical providers) of pain. The vi gnetteand retrospect ive-based methodologies most frequently employed in such investigatio ns impose constraints on the research questions that could be addressed and th e conclusions that have been yielded. An innovative research design that capitalizes on the advantages of these methodologies while limiting their disadvantages may further our unde rstanding of these complex issues. Further, a more detailed analysis of the clinical decision making process itself than has heretofore been conducted would

PAGE 32

32 be a positive direction for this lin e of research. Such an analysis would extend past the practice of mere examination of the end result i. e., the decision produc t and instead permit investigation of the process that precedes the result. Only through analysis of the process can we begin to understand where biases and knowledge de ficits infiltrate clinical decision-making. Investigations of this type have the potential to inform future intervention efforts aimed at rectifying such problems. In addi tion, to date, little work has examined the interaction of the characteristics of both the observe r and the observed in this contex t. This is largely due to the difficulty in securing participants of suffici ent variability to conduct adequately powered analyses. Thus, investigations like the current one that particularly target pr oviders of variable personal and professional char acteristics are needed.

PAGE 33

33 CHAPTER 2 METHODS Participants All participants were at leas t 18 years of age and a license d practicing Registered Nurse (RN). Students and those with advanced nursi ng degrees were included if they met the aforementioned criteria. Recruitmen t of participants occurred at the local and national level. Local recruitment strategies incl uded presentations at class lectur es, advertisements displayed in local hospitals and clinics, and presentations at local and state association meetings. National recruitment occurred via nursing mailing lists an d email listservs, and through attendance at national meetings. It was expect ed that this approach would maximize the demographic and clinical diversity of practici ng professionals. Continuing educa tion credits for the debriefing portion of this study or financial compensation served as incentives for participation. The current study was powered for the idiograp hic analyses of the lens model approach. Task sensitivity, the principal c oncern of lens model designs, is primarily a function of the ratio of profiles to cues. The smallest recommended prof ile-to-cue ratio is 5:1, but a 10:1 ratio may be preferred given logistical f easibility (Cooksey, 1996). Idiogra phic power of this study was maximized by employing a ratio that exceeded the acceptable 5:1 ratio. The 10:1 ratio would likely have imposed undue burden on study partic ipants through the crea tion of a large number of profiles and, thus, was not adopted. This study investigated 4 contextual cues (age, race, sex, and pain expression) and used a total of 32 profiles, which is a profile-to-cue ratio of 8:1. This ratio permitted each possible cue combination to be presented twice, which further enhanced statistical power. It was expect ed that this ratio would ensure adequately powered nomothetic analyses when the idiographic data were aggreg ated. Lens model designs that employ a sufficient profile-to-cue ratio have enhanced power at the no mothetic level due to greater reliability of each

PAGE 34

34 individuals data as a result of multiple observa tions. Thus, policy-capturing investigations like the current study can achieve adequate power with a smaller sample size than traditional research designs (Cooksey, 1996). Given the methodological uniqueness of the pr oposed study, it was difficult to conduct a precise traditional power anal ysis at the nomothetic level. However, based on a modified power analysis using Power Analysis and Sample Size (PASS) software, the results of a previous study (Campbell, 2002) that most closely resembles the current one, and the literature reviewed above, a total of 50 participants were planned for recruitment. Measures Demographics Questionnaire A demographics questionnaire elicited informa tion pertaining to pa rticipant sex, age, race/ethnicity, years of professi onal nursing experience, and past pr actice settings and clinical specialties. Gender Role Expectations of Pain The GREP (Robinson et al., 2001) is comprised of 12 visual analog scales (VAS) that assess an individuals view of the typical man and woman with respect to pain sensitivity, pain endurance, and willingness to report pain. It also assesses the indi viduals personal attribution of his or her pain sensitivity, pain endurance, and willingness to repor t pain relative to the typical man and woman. Psychometric pr operties of the GREP are s ound. The factor structure is consistent with the theoretical fo rmulation of the scales and acc ounts for 76% of the variance in scores. The GREP has good test-rete st reliability with individual item correlations ranging from .53 to .93. High correlations (-.71 to -.81) betw een individual items reflecting the opposite gender role (i.e., typical male endurance of pa in correlated with typical female endurance) demonstrates internal consiste ncy. Finally, sex differences in the endorsement of items on the GREP are large, with the larges t differences (46% of variance) shown for willingness to report

PAGE 35

35 pain items. These differences provide evidence for the construct vali dity of the measure (Robinson et al., 2001). The GREP ha s also been demonstrated to be a significant predictor of experimental pain ratings in undergraduate men and women, account ing for a significant proportion of the sex differences in pain report (Wise et al., 2002). Consistent with previous research (Robinson and Wise, 2003), two theoreti cally important items from the GREP were included in the subsequent analyses to determin e if gender stereotypes about endurance of pain and willingness to report pain influence clin ical decisions regardi ng pain assessment and management practices. Lens Model Design The current study employed a lens model de sign, an analogue method for capturing how individuals use information in th eir environment to form judgment s. The lens model serves as a theoretical model of how individuals use information to make judgments, and as an experimental paradigm for studying judgment processes and outcomes (Beal et al., 1978). The lens model approach is based on the assumption that the i mmediate environmental context i.e., the cues available to the individuals pe rceptual faculties influences an individuals judgment process. Empirical applications of this a pproach typically consist of a se ries of cue-containing profiles presented to a study participant, about which the participant forms a judgment. This judgment is recorded via a quantifiable res ponse mode, such as a VAS. In the current study, the outcome of the judgment process was each participants assessment ratings of pain and mood, as well as his/her decisions regarding pain management practices. The contextual cues of interest that vary systematically acros s clinical profiles are patient age, sex, race, an d expression of pain.

PAGE 36

36 Profiles Each profile consists of a vignette and vide o clip. The vignettes contain patient clinical information indicating the status of the patient, pain compla int (duration and location), and prescription medication orders. The majority of th e patient clinical information is included only to enhance task familiarity and ecological validit y; this information has minimal variability and is within normal limits. The remaining information is used to provide the participants with the context in which they are to make assessment and treatment decisions. The video clips were generated with People Putty software by Haptek Incorporated. People Putty is a technology that permits the user to de velop virtual characters with a variety of features. Standard characters are available for use; however, users may also upload digital picture files of actual people and program thes e files into the existing software. Various demographic features (e.g., sex, age, and race) can be manipulated to create a diverse array of characters. A particularly innovati ve and desirable feature of this technology is the ability to manipulate the facial expressions of characters. Us ers can manipulate specific facial features in order to achieve a desired expres sion. The specific features of an empirically-validated pain expression brow lowered, cheek raised and lid tightened, nose wrinkled and upper lip raised, and eye closure can be altered to represent varying degrees of pain expressivity. Furthermore, these expressions can then be held constant and applied to other characters of different characteristics. For example, the characteristic features of a high pa in face can be equally applied to both a young, African-American male a nd elderly, Caucasian female. This feature of People Putty permits a level of experimental control that is lacking in retrospective-based research, and permits a level of ecological validity that is lacking in vign ette-based research. In this manner, the current design sought to maxi mize the advantages of these approaches while minimizing their disadvantages. The control afford ed by the virtual patient technology is also

PAGE 37

37 greater than that avai lable through the use of actual person s who have been attempted to be equated on demographic characteristics and trained in the display of facial expressions of pain. Another important advantage over the use of trai ned actors is that the virtual patient technology eliminates from the development of the stimuli the very biases this study is intending to investigate. Profiles were presented randomly to control for order effects. Each profile contains four cues: sex (male, female), age (young adult, old adult), race (Caucasian, African-American), and pain expression (low, high). The cues of patient se x, age, and race were available to participants from the video clips. Pain intensity level wa s represented by the facial expressions of the characters. As noted above, thes e facial expressions were gene rated through manipulation of the specific features characteristic of the pain face. The FACS was used to direct the creation of these expressions. A total of 16 unique scenario s were created representing all possible cue combinations. In the current study, in order to ach ieve maximal task sens itivity, each participant viewed each possible cue combination twice, for a total of 32 profiles. Judgments Four assessment ratings were obtained for each profile presented. Par ticipants rated each virtual patients level of pain intensity and pain unpleasantness, as well as their level of positive and negative mood. Pain assessment ratings were recorded on separate VAS s with endpoints at no pain sensation and most intense pain sensation imaginable for pain intensity, and not at all unpleasant and most unpleasant imaginable for pain unpleasantness. Mood assessment ratings were recorded on VAS s with endpoints at neutral and most positive imaginable for positive mood, and neutral and most negative imaginable for negative mood. Four treatment ratings were also obtained for each profile: (1) likelihood of administering a non-opioid analgesic within prescribed dosage, (2) likelihood of administering an opioid analge sic within prescribed dosage,

PAGE 38

38 (3) likelihood of recommending a change in non-opio id analgesia to achieve better pain control, and (4) likelihood of recommending a change in opioid analgesia to achieve better pain control. Separate VASs were used for each rating, with endpoints at not at all likely and complete certainty The VASs for assessment and treatment rati ngs consist of comput erized horizontal lines anchored by their correspond ing endpoint descriptors. Participants used a slider to indicate the point that best represents their assessment and treatment ratings. The distance from the left-most endpoint to the point indicated by the participant represents their ratings. Procedure A WEB-based delivery model was used for the current study. Each participant was asked to read an informed consent that included a de scription of the study, time required to complete the study, and compensation for thei r time if they decided to part icipate. Participants provided electronic consent if they agreed to participate. After providing consent, participants completed the demographics questionnaire. The order of th e GREP and patient prof ile administration was counterbalanced and followed the completion of the demographics questionnaire. The following procedure was used for all administrations of th e patient profiles: (1) participants read the clinical information and view the video simultane ously; (2) participants complete questions that ask them to provide pain and mood ratings usi ng electronic VASs, and ra te the likelihood of carrying out pain management practices. Prior to the patient profile portion of the study, participants read an instructions document that informed them about how to approach the task and how to use the electronic VASs to give pain mood, and treatment ratings. Participants are instructed to fully complete the questions for each profile and are not permitted to return to previously completed profiles. To maximize comp liance with instructions and provide answers to frequently posed questions, a help menu was provided and accessible at all times.

PAGE 39

39 Following completion of the study, participants were administered a short task-validity questionnaire that asked them to guess at the purpose(s) and/or hypothesi s(es) of the study. They were also asked about what information they used when making their assessment and treatment ratings. Participants were then fully debriefed regarding the variables of interest and the study hypotheses. A brief educational tuto rial regarding pain practice with sex, age, and ethnically diverse patients was then provided, after which participants completed a short test of their knowledge in this area. All data were collected and stored in an electr onic database. The time necessary to complete the study varied between approximately 60 and 90 minutes, and was primarily a function of individual part icipants computer specifications. Hypotheses Pain Assessment 1A. There is a discrepancy between the experiment al and clinical literature concerning the effect of patient sex on pain assessment. Th e Robinson and Wise (2003) hypothesis that gender role stereotypes will result in obs ervers rating males pain lower will be supported in the current study by pain assessment ratings for male virtual patients being reliably lower than those for female virtual patients. Conversel y, the clinical pain literature indicates that females are at increased risk of having their pain under-assessed. This may be due to provider beliefs about sex differences in pain perception, the fact that th e appraisal of pain may be more difficult for patients who are not of the same sex (primar ily male providers were included in these investigations), and/or some ot her as-yet undetermined reason. S upport for these results will be seen in the current study by pain assessment ratin gs for female virtual patients being reliably lower than those for male virtual patients. 1B. Elderly patients will be judged to have lo wer pain intensity a nd unpleasantness than young patients.

PAGE 40

40 1C. African-American patients will be judged to have lower pain intensity and unpleasantness than Caucasian patients. 1D. Patients demonstrating a low facial expressi on of pain will be judged to have lower pain intensity and unpleasantness than patients de monstrating a high facial expression of pain. Treatment with Non-opioid Medication 2A. Patient sex cues will predict likeli hood of administering non-opioid medication. Specifically, providers will be less likely to util ize this treatment modality with female patients relative to male patients. 2B. Patient age cues will predict likelihood of administering non-opioid medication. Specifically, providers will be less likely to util ize this treatment modality with elderly patients relative to younger patients. 2C. Patient race cues will predict likeli hood of administering non-opioid medication. Specifically, providers will be less likely to utilize this treatment modality with AfricanAmerican patients relative to Caucasian patients. 2D. Patient pain expression cues will pr edict likelihood of ad ministering non-opioid medication. Specifically, providers will be less lik ely to utilize this treatment modality with patients demonstrating a low facial expression of pain relative to patients demonstrating a high facial expression of pain. Treatment with Opioid Medication 3A. Patient sex cues will predict like lihood of administering opioid medication. Specifically, providers will be less likely to util ize this treatment modality with female patients relative to male patients.

PAGE 41

41 3B. Patient age cues will predict like lihood of administering opioid medication. Specifically, providers will be less likely to util ize this treatment modality with elderly patients relative to younger patients. 3C. Patient race cues will predict like lihood of administering opioid medication. Specifically, providers will be less likely to utilize this treatment modality with AfricanAmerican patients relative to Caucasian patients. 3D. Patient pain expression cues will pr edict likelihood of administering opioid medication. Specifically, providers will be less lik ely to utilize this treatment modality with patients demonstrating a low facial expression of pain relative to patients demonstrating a high facial expression of pain. Recommendations for Change in Medications 4A. Patient sex cues will predict likelihood of recommending a change in both non-opioid and opioid medications. Specifically, providers w ill be less likely to make recommendations on behalf of female patients re lative to male patients. 4B. Patient age cues will predict likelihood of recommending a change in both non-opioid and opioid medications. Specifically, providers w ill be less likely to make recommendations on behalf of elderly patients relative to younger patients. 4C. Patient ethnicity cues will predict like lihood of recommending a change in both nonopioid and opioid medications. Specificall y, providers will be le ss likely to make recommendations on behalf of African-American patients relative to Caucasian patients. 4D. Patient pain expression cues will predic t likelihood of recomme nding a change in both non-opioid and opioid medications Specifically, providers wi ll be less likely to make recommendations on behalf of patients demonstrati ng a low facial expressi on of pain relative to patients demonstrating a high facial expression of pain.

PAGE 42

42 Healthcare Provider Characteristics There are few empirical investigations of the influence of healthcare provider characteristics on pain assessment and pain manage ment practices. There has also been a lack of theoretical attention to these issues. Consequent ly, specific, empirically and/or theoretically informed hypotheses concerning the influence of provider characteristics on pain assessment and treatment are not proposed. To the extent that variability in the characteristics of providers who participate in the current study permits, exploratory analyses will be conducted. Statistical Analyses Descriptive statistics were conducted to summarize th e demographic and background characteristics of the sample. Idiographic Simultaneous multiple regression equations were generated for each individual to capture his/her decision making policies. Virtual patient age, race, sex, and pain expression served as independent variables in each model. Pain and mood assessment ratings, medication-based treatment ratings, and change-related recommenda tion ratings were dependent variables in their respective models. The standardized regression coe fficients in each equation represent the weight of each cue in the formation of the assessment a nd treatment judgments. This weight represents the unique contribution and relative importance of each cue in the participants clinical decision. The coefficient of multiple determination ( R ) represents the amount of variance in assessment and treatment decision policies accounted for by th e predictor variables, or the overall function of the cues in each individuals policy. Nomothetic Following idiographic analyses for all particip ants, descriptive statis tics were conducted to determine: 1) the total number of cues that we re significantly weighted at each decision policy;

PAGE 43

43 2) the amount of variance accounted for by each cu e in the separate deci sion policies; 3) the number of significant overall deci sion policies; and 4) the averag e coefficient of determination for each decision policy. Paired samples t-test s compared ratings within cue for the entire sample. Finally, Chi-square test s and Analysis of Variance (ANOV A) were conducted to explore whether participant demographic and professional background char acteristics were related to overall pain assessment and treatment pol icies and contextual cue utilization.

PAGE 44

44 CHAPTER 3 RESULTS Participants Fifty-four nurses participated in this study. Consistent with na tional data, the vast majority of nurses were female (83%) and self-reported Ca ucasian (93%). The average age of the sample was approximately 42 years ( SD = 11.90). A wide range of geographical locations was represented, with Florida ( n = 23) being the modal state of re sidence. Twenty-two participants held an Associate Degree in nursing, whereas 17 ma triculated at the Bachelor level and 15 at the graduate level. At the time of their participa tion, approximately 72% were not currently enrolled in an academic program. Of the 15 nurses who were currently students, the majority ( n = 11) was pursuing graduate degrees. Examination of se lf-reported professional b ackground data indicated that the average years of nursing experience was approximately 14 ( SD = 10.52). The three most frequently endorsed current prac tice areas were critical care ( n = 22), primary care ( n = 16), and oncology ( n = 14). With one exception, all nurses reported experience working in a hospital setting. Detailed demographic and background information is provided in Table 3-1. Pain Assessment Policies Patient sex, race, age, and pain expressi on were hypothesized to be significant, independent factors in nurses assessments of pa in intensity and unpleasan tness. Specifically, it was expected that, relative to their within -cue counterparts, lower pain intensity and unpleasantness ratings would be assigned to patients who were African-American, older, and displaying high levels of pain expressivity. Comp eting hypotheses were ar ticulated regarding the influence of patient sex on pain assessment ratings.

PAGE 45

45 Table 3-1. Demographic and background characteristics of participants N % of total Mean ( SD ) Range Sex Female Male 45 9 83.3 16.7 Age (years) 42.02 (11.90) 22 66 Race Caucasian African-American Asian Other 50 2 1 1 92.6 3.7 1.9 1.9 Nursing education Associates degree Bachelors degree Graduate degree 22 17 15 40.7 31.5 27.8 Current educational status Not enrolled Enrolled 39 15 72.2 27.8 Nursing experience (years) 14.06 (10.52) 0 37 Practice area Critical Care Emergency Oncology Medical-Surgical Internal Medicine Pediatrics Primary Care Obstetrics Psychiatry Hospice Other 9 7 7 6 4 4 3 2 2 1 9 16.7 13.0 13.0 11.1 7.4 7.4 5.6 3.7 3.7 1.9 16.7 Practice setting* Hospital Outpatient facility Nursing home Hospice 53 16 8 5 98.1 29.6 14.8 9.3 *Categories are not mutually exclusive. Pain Intensity Results indicated that 34 nurses had significant ( p < .05) policies for pain intensity assessment; 5 had policies that approached significance ( p < .1). Thirteen of these 39 nurses used sex as a prominent ( p < .1) cue in their policy. Ten gave high er pain intensity ratings for females; the reverse was true for 3 nurses. Race was a pr ominent cue in the policies of 8 of these 39

PAGE 46

46 nurses, with 7 more likely to judge higher pain intensity in African-American virtual patients and 1 more likely to judge higher pain intensity in Caucasians. Thirteen nurses used age as a prominent cue in their pain intensity assessment po licies. Twelve were more likely to judge older virtual patients as experiencing gr eater pain intensity, whereas th e converse was true for 1 nurse. Finally, with the exceptio n of 1, pain expression was a promin ent cue for all nurses, such that virtual patients displaying high le vels of pain expression were j udged to be experiencing greater levels of pain by the participants. Results of id iographic regression analyses for pain intensity assessments are presented in Table A-1. Pain Unpleasantness Similar results were obtained for pain unpleasantness ratings, su ch that such that 35 nurses had significant ( p < .05) policies, and 2 had policie s that approached significance ( p < .1). Examination of the contribution of the specific contextual cues i ndicated that sex, race, age, and pain expression were prominent cues in th e policies of 13, 8, 13, and 36 of these nurses, respectively. Eleven of the nurse s with a significant sex cue were more inclined to make higher ratings for female patients; the converse was true for 2 nurses. African-American virtual patients were assessed to be experiencing more pain unp leasantness by 7 nurses, whereas 1 nurse judged Caucasian patients to be experiencing more pain. Relative to younger pa tients, older patients were judged to be experiencing more pain unpl easantness by 12 nurses; the opposite was true for 1 nurse. Finally, every nurse with a prominen t pain expression cue judged those with high expressivity to be experiencing greater pain unpleasantness than those with low expressivity. Results of idiographic regression analyses for pain unpleasantness assess ments are presented in Table A-2.

PAGE 47

47 Mood Assessment Policies Positive Mood Twenty-three nurses had significant ( p < .05) policies for positive mood assessment; 2 had policies that approached significance ( p < .1). The 3 nurses in whom sex played a prominent role in their positive mood policies each judged male patients to be experiencing greater positive mood relative to female patients. Six nurses used race as a prominent cue; 4 were more likely to judge Caucasian patients as having greater positi ve mood, whereas 2 indicat ed this for AfricanAmericans. Thirteen had policies in which ag e was a prominent cue. Of these 13 nurses, 12 assessed younger virtual patients to be experi encing greater positive mo od relative to older patients. Conversely, 1 nurse assessed older pati ents to be experiencing greater positive mood. Twenty-four nurses used pain expression as a pr ominent cue; all were more likely to assign greater ratings to patients with low expressivity. Results of id iographic regression analyses for positive mood assessments are presented in Table A-3. Negative Mood Thirty-five nurses had significant ( p < .05) policies for negative mood assessment; 3 had policies that approached significance ( p < .1). Regarding sex, 12 nurses had policies in which this cue was prominent. Ten nurses judged female patients to be expe riencing greater negative mood compared to males, whereas the converse wa s true for 2 nurses. Of the 8 nurses who used race as a significant cue, 6 assigned greater negative mood ratings for African-American patients. The remaining 2 gave greater ratings fo r Caucasian patients. All 13 nurses with policies in which age was a prominent cue assessed grea ter negative mood in older virtual patients relative to younger patients. Fina lly, all 34 nurses who used pain expression as a prominent cue assigned greater negative mood ratings to those with high expressivity. Results of idiographic regression analyses for negative mood assessments are presented in Table A-4.

PAGE 48

48 Treatment Decision Policies The patient cues of sex, race, age, and pain expression were each hypothesized to play a significant, unique role in policies regarding administra tion of non-opioid and opioid medications. Specifically, a grea ter likelihood of medication admi nistration (both non-opioid and opioid) was expected for patient s who were male, Caucasian, younge r, and displaying high pain expressivity. Non-opioid Medication Twelve nurses had significant ( p < .05) policies for non-opioid treatment; 3 had policies that approached significance ( p < .1). Sex played a prominent role for 2 nurses out of the 15 with significant overall non-opioid policies. Both of these nurses gave higher ratings to female patients and, thus, were more likely to engage in this treatment practice with them compared to male patients. Three nurses used patient race as a prominent cue. African-American patients were more likely to be admini stered a non-opioid medication by 2 nurses. One nurse was more likely to engage in this practice with Caucasian patients. Age was a prominent cue in the policies of 6 of these nurses. Four were more likely to administer non-opioid medication to younger patients; 2 were more likely to engage in this treatment practice for olde r patients. Lastly, pain expression was a prominent cue in the policies of 13 nurses, with low expressive virtual patients being more likely to receive non-opioid medi cation by 7 nurses and high expressive patients more likely to receive this treatment by 6 nurse s. Additional, unplanned analyses were conducted in response to the disparate dir ectional effect of the pain expr ession cue between non-opioid and opioid treatment domains. As noted below, ever y nurse who significantly weighted patient pain expression when making opioid treatment ratings used this cue in a similar manner; high expression patients received higher ratings than lo w expression patients. Since the direction of this effect was approximately equal for non-opioi d decisions, follow-up anal yses tested whether

PAGE 49

49 opioid ratings differed between these two nonopioid groups (i.e., those who used pain expression in a positive vs. nega tive way). Results i ndicated that the tw o non-opioid groups did not provide significantly different ratings for high and low expression patients in regards to opioid treatment [ F (1,11) = .39, p > .05]. Idiographic regression results for non-opioid treatment policies are presented in Table A-5. Opioid Medication Twenty-three nurses had significant ( p < .05) policies for opioid treatment; 4 had policies that approached significance ( p < .1). Sex was a prominent cue in the opioid treatment policies of 7 nurses. Of these, 6 were more likely to engage in this treatment with female patients; 1 was more likely to do so with male patients. All 5 nurses with a prominent race cue were more likely to administer opioid treatment to African-Ame rican patients. Of the 9 who used age as a prominent cue, 8 were more likely to engage in this treatment with older versus younger patients. The converse was true for 1 nurse. All of the 25 nurses with a pr ominent pain expressivity cue were more likely to administer opioid medicati on to highly expressive patients. Idiographic regression results for opioid treatment policies are presented in Table A-6. Recommendation Policies All four patient cues were hypothesized to ex ert a significant, inde pendent influence on policies regarding recommendations for a change in non-opioid and opioid medication. Specifically, for both classes of medication, it was expected that nurses would be less likely to make such recommendations for patients who were female, African-American, older, and displaying high levels of pain expressivity. Change in Non-opioid Medication Fifteen nurses had significant ( p < .05) policies for recommending a change in non-opioid treatment; 3 had policies that approached significance ( p < .1). Of these 18, sex was a prominent

PAGE 50

50 cue for 3, race was a prominent cue for 7, age wa s a prominent cue for 3, and pain expression was a prominent cue for 16. Two nurses were more likely to make recomm endations on behalf of female patients; 1 nurse was more likely to do so on behalf of male patients. Four nurses were more likely to recommend a change for Af rican-American patients. A recommendation was more likely for Caucasian patients in 3 nurses. Two nurses were more likely to recommend a change for younger patients than older, wher eas the converse was true for 1 nurse. Finally, patients with a high level of pain expressivity were more likely to have recommendations made on their behalf by 13 of the nurse s. Three nurses were more likel y to recommend a change for patients with low pain expressi ons. Table A-7 presents the re sults of idiographic regression analyses for policies regarding recommenda tions of change in non-opioid medication. Change in Opioid Medication Sixteen nurses ha d significant ( p < .05) policies for recommending a change in non-opioid treatment; 6 had policies that approached significance ( p < .1). Sex was a prominent cue in the policies of 3 of these 22. Change recommendations were more likely for female patients among 2 nurses; the converse was true for 1 nurse. All of the 4 that used race as a prominent cue were more likely to make recommendations for Afri can-American patients relative to Caucasian. Seven nurses had policies in which age was a prom inent cue. Five nurses were more likely to recommend a change for older patients. Two we re more likely to make a recommendation on behalf of younger patients. Rega rding pain expressi on, all of the 20 nurses with a prominent expression cue were more likely to recommend a change in opioid treatment for patients displaying a high pain expression compared to th ose displaying a low pain expression. Table A-8 presents the results of idiogra phic regression analyses for policie s regarding recommendations of change in opioid medication.

PAGE 51

51 Number of Significant Cues Descriptive and frequency data were generate d for the total number of cues that were significantly weighted at each decision policy (Tab le 3-2). Participants with invariant policies were excluded from these analyses. On average, a greater number of cues were used for pain and mood assessment policies (pain intensity: M = 1.51, SD = 1.01; pain unpleasantness: M = 1.45, SD = 1.01; positive mood: M = 1.27, SD = .88; negative mood: M = 1.36, SD = 1.00). Decision policies about non-opioid treatment had the leas t number of significan t cues (non-opioid treatment: M = .66, SD = .80; non-opioid recommendation: M = .76, SD = .89). Results of frequency analyses indicated that for pain and mood assessments, the majority of participants significantly weighted one or two cues in their policies. For d ecisions (treatment and change) regarding non-opioid medication, at least half of partic ipants did not have a significant cue in their policies, and over 75% used 1 or fewer cu es. Approximately 70% of nurses had an opioid treatment policy with 1 or 2 significant cues, whereas the majority of nurses significantly weighted 1 or fewer cues in their decisions about recommending a chan ge in opioid medication. Significance of Contextual Cues In order to quantify the amount of variance accounted for by each cue in the various decision policies, individual standardized regres sion coefficients for each cue within each policy across nurse were squared. Results of these calc ulations indicated that sex, race, age, and expression cues accounted for as much as 13%, 15 %, 26% and 77%, respectiv ely, of the variance in policies for pain intensity assessments and 14%, 13%, 28%, and 79%, respectively, of the variance in policies for pain unpl easantness assessments. In regards to mood assessments, these cues accounted for as much as 15%, 15%, 22%, and 62%, respectively, of the variance in positive mood policies and 20%, 14%, 27%, and 81%, respectively, of the variance in negative mood policies. Examination of the regression co efficients for medication-related treatment

PAGE 52

52Table 3-2. Number of significant cues at each policy Number of significant cues 0 1 2 3 4 Decision policy N Mean SD Min Max N (%) N (%) N (%) N (%) N (%) Pain intensity assessment 53 1.51 1.01 0 4 8(15.1) 21(39.6) 14(26.4) 9(17.0) 1(1.9) Pain unpleasantness assessment 53 1.45 1.01 0 4 9(17.0) 21(39.6) 14(26.4) 8(15.1) 1(1.9) Positive mood assessment 49 1.27 .88 0 3 9(18.4) 23(46.9) 12(24.5) 5(10.2) 0(0) Negative mood assessment 53 1.36 1.00 0 4 10(18.9) 22(41.5) 15(28.3) 4(7.5) 2(3.8) Non-opioid treatment 50 .66 .80 0 2 27(54.0) 13(26.0) 10(20.0) 0(0.0) 0(0) Opioid treatment 51 1.16 .86 0 3 12(23.5) 22(43.1) 14(27.5) 3(5.9) 0(0) Recommendation: non-opioid 50 .76 .89 0 3 25(50.0) 14(28.0) 9(18.0) 2(4.0) 0(0) Recommendation: opioid 52 .96 .82 0 3 16(30.8) 24(46.2) 10(19.2) 2(3.8) 0(0)

PAGE 53

53 policies revealed that sex, race, age, and e xpression cues accounted for a maximum of 21%, 30%, 26%, and 38%, respectively, of the variance in non-opioid decisions and 17%, 22%, 14%, and 85%, respectively, of the variance in opioid decisions. Finally, simila r calculations were made for the two recommendation policies. Resu lts indicated that up to 25%, 13%, 22%, and 34% of the variance in recommendations for a change in non-opioid treatment and 14%, 15%, 17%, and 68% of the variance in recommendations for a change in opioid treatment were accounted for by the patient cues of sex, race age, and expression, respectively. Table 3-3 contains detailed results of these calculations. Table 3-3. Variance in decision polic ies explained by contextual cues Sex Race Age Pain expression Decision policy Mean ( SD ) Range Mean ( SD ) Range Mean ( SD ) Range Mean ( SD ) Range Pain intensity assessment .03(.04) .00-.13 .03(.04) .00-.15 .04(.05) .00-.26 .29(.22) .00-.77 Pain unpleasantness assessment .04(.04) .00-.14 .03(.03) .00-.13 .05(.06) .00-.28 .30(.23) .00-.79 Positive mood assessment .03(.04) .00-.15 .03(.04) .00-.15 .06(.06) .00-.22 .17(.16) .00-.62 Negative mood assessment .04(.05) .00-.20 .03(.05) .00-.14 .04(.05) .00-.27 .26(.22) .00-.81 Non-opioid treatment .03(.04) .00-.21 .04(.05) .00-.30 .04(.05) .00-.26 .10(.11) .00-.38 Opioid treatment .04(.04) .00-.17 .04(.05) .00-.22 .04(.04) .00-.14 .23(.23) .00-.85 Recommendation: non-opioid .03(.05) .00-.25 .04(.03) .00-.13 .04(.05) .00-.22 .12(.15) .00-.34 Recommendation: opioid .03(.03) .00-.14 .03(.04) .00-.15 .03(.04) .00-.17 .17(.19) .00-.68 Note: Values represent squared standardized regression coefficients. Number and Significance of Overall Policies Descriptive and frequency data we re generated for the entire sa mple at the level of overall policy ( R ). Out of eight total assessment and treatment decisions, participants had an average of 3.56 ( SD = 2.77; Range: 0-8) significant ( p < .1) decision policies. The modal number of significant policies was 1 ( n = 10), with the next mo st frequent being 8 ( n = 8), 3 ( n = 7), 4

PAGE 54

54 ( n = 7), and 0 ( n = 7) significant policies. Six particip ants had 2 significant policies across the study; 5 nurses had 7 significant policies with the remaining nurses having 6 ( n = 2) and 5 ( n = 2) significant policies. These data indicate the presence of variabi lity across the assessment and treatment ratings in terms of which particip ants had significant overa ll decision policies. Descriptive data were generated for the i ndividual decision policie s across participants. As can be seen in Table 3-4, ther e is wide variability in overall R s, both within and between policies. On average, a greater amount of total variance was accounted for in pain assessment policies (pain intensity: M = .40; pain unpleasantness: M = .41) than policies pertaining to treatment and recommendation decisions. The least amount of variance ( M = .21) was accounted for in decisions about non-opioid treatment. Table 3-4. Descriptive data on overall policy capturing Decision policy Mean SD Min Max Pain intensity assessment .40 .22 .02 .82 Pain unpleasantness assessment .41 .24 .03 .84 Positive mood assessment .29 .17 .02 .70 Negative mood assessment .37 .22 .01 .83 Non-opioid treatment .21 .12 .05 .51 Opioid treatment .34 .22 .03 .86 Recommendation: non-opioid .23 .16 .01 .62 Recommendation: opioid .26 .19 .00 .73 Note: Values represent coefficients of determination ( R ). Within-cue Comparisons For each nurse, average assessment and treatmen t ratings were calculated across virtual patients at each level of cue (Table 3-5). Paired samples t-tests were then used to compare ratings within cue for the entire sample. Pain Assessment For pain intensity and unpleasantness ratings, significant differences were present within each cue. Nurses assessed female patients to be experiencing great er pain intensity [ t (46) = -3.83, p < .001, d = .76] and unpleasantness [ t (46) = -4.22, p < .001, d = .90] than male patients.

PAGE 55

55Table 3-5. Means and standard devi ations for ratings within cue Sex Age Race Expression Male Female Young Old Caucasian AfricanAmerican Low High Pain intensity assessment 41.95 (20.97) 45.01 (20.48) 41.20 (20.37) 45.76 (21.15) 41.68 (21.19) 45.29 (20.23) 34.44 (22.56) 52.54 (20.81) Pain unpleasantness assessment 43.05 (21.41) 46.74 (20.23) 42.39 (20.58) 47.40 (21.15) 43.39 (21.34) 46.40 (20.29) 35.33 (23.25) 54.47 (20.92) Positive mood assessment 14.37 (12.96) 13.44 (11.47) 15.88 (13.12) 11.93 (11.54) 14.16 (12.62) 13.66 (11.93) 18.33 (15.39) 9.47 (10.80) Negative mood assessment 38.02 (22.00) 41.74 (20.64) 37.45 (21.18) 42.32 (21.28) 38.85 (21.26) 40.92 (21.31) 30.61 (23.22) 49.18 (21.56) Non-opioid treatment 61.04 (34.62) 61.89 (34.05) 61.91 (33.52) 61.01 (35.30) 61.70 (34.43) 61.22 (34.24) 62.28 (33.44) 60.62 (36.43) Opioid treatment 55.40 (30.44) 59.09 (28.58) 55.11 (29.74) 59.38 (29.32) 55.50 (30.40) 58.98 (28.72) 47.68 (34.71) 66.82 (26.99) Recommendation: non-opioid 39.60 (32.62) 41.75 (32.12) 40.15 (32.19) 41.19 (32.61) 40.93 (33.19) 40.41 (31.57) 38.39 (32.09) 42.94 (33.78) Recommendation: opioid 37.43 (30.59) 39.93 (30.73) 37.54 (29.98) 39.83 (31.43) 37.11 (30.66) 40.25 (30.64) 32.49 (32.02) 44.89 (30.67) Note: Rating scale is 0100.

PAGE 56

56 Greater pain intensity [ t (46) = -4.06, p < .001, d = .93] and unpleasantness [ t (46) = -3.54, p < .01, d = .74] ratings were assigned to African-Ameri can versus Caucasian patients. Older patients were judged to be experiencing greater pain intensity [ t (46) = -4.34, p < .001, d = 1.05] and unpleasantness [ t (46) = -4.87, p < .001, d = 1.10] than younger patients. Finally, patients with high expressivity were judged to be experiencing greater pain intensity [ t (46) = -8.21, p < .001, d = 1.83] and unpleasantness [ t (46) = -7.88, p < .001, d = 2.94] than those with low pain expressivity. Follow-up analyses were conducted to test the a priori hypothesis that gender-role expectations about pain would in fluence participants assessment ratings. Si nce within-cue sex differences emerged for both pain intensity and unpleasantness ratings, follow-up analyses were conducted separately for these decision domains. Correlation analyses indicated that the GREP factor of willingness to report pain was significa ntly associated with average pain intensity assessment ratings for both male ( r = .31, p < .05) and female ( r = .30, p < .05) virtual patients. This factor was also significan tly associated with average pain unpleasantness ratings for female patients ( r = .27, p < .05); results approached significance ( r = .27, p = .052) for male patients. The GREP factor of s tereotypic endurance for pain was not signifi cantly associated with average pain intensity or unpleasan tness ratings for either male or female patients; however, the magnitude of these relationships (coefficient rang e: -.18 to -.23) was sufficient as to warrant follow-up analyses. Analysis of Covariance (ANC OVA) results indicated that the significant within-cue sex differences persisted [pain intensity: F (1,51) = 10.73, p < .01; pain unpleasantness: F (1,51) = 13.24, p < .01] even after controlli ng for the GREP factors of willingness to report pain and stereotypic endurance for pain. Mood Assessment Significant differences in averaged positive mood assessment ratings emerged within age and expression cues, with younger [ t (46) = 4.44, p < .001, d = .96] and low expression [ t (46) =

PAGE 57

57 4.90, p < .001, d = 1.16] patients receiving higher rati ngs than those who were older and displaying high pain expression. No differences in positive mood ratings were evident between races or sexes. Turning to assessment of nega tive mood, nurses rated female virtual patients as having greater negative mood than male patients [ t (46) = -3.38, p < .01, d = .71]; older patients as having greater negative mood than younger patients [ t (46) = -4.73, p < .001, d = 1.08]; and high expression patients as ha ving greater negative mood than low expression patients [ t (46) = -7.96, p < .001, d = 1.69]. A trend was also observed for African-American patients to receive higher negative mood ratings than Caucasian patients [ t (46) = -1.85, p < .07, d = .42]. Treatment Decisions There were no significant di fferences within cue for nonopioid treatment decisions. Opioid treatment decisions were more likely to be endorsed for patients who were female [ t (46) = -2.75, p < .01, d = .70], African-American [ t (46) = -2.39, p < .05, d = .59], older [ t (46) = -3.26, p < .01, d = .77], and displaying high expressivity [ t (46) = -6.25, p < .001, d = 1.38]. Follow-up analyses were conducted to test the a priori hypothesis that gender-role expectations about pain would influence partic ipants treatment decisions. Thes e analyses were confined to ratings for opioid treatment, since sex differe nces only emerged for this treatment domain. Correlation analyses indicated no significant association be tween the GREP factor of willingness to report pain and average opioid treatment ratings for male ( r = .14, p > .05) and female ( r = .15, p > .05) virtual patients. Similarly, no significant association emerged between the GREP factor of stereotypic endurance for pain and average opioid treatment ratings for male (r = .03, p > .05) and female ( r = .08, p > .05) patients. Due to these non-significant findings, no further analyses were conducted to control for the eff ects of gender-role expectations about pain.

PAGE 58

58 Recommendations No significant differences emerged within race and age cues for decisions regarding recommendations for a change in non-opioid medi cation. Differences were evident, however, for sex and pain expression cues. Nurses were mo re likely to make change recommendations on behalf of female [ t (46) = -1.98, p = .05, d = .42] patients and those displaying high levels of pain expression [ t (46) = -2.40, p < .05, d = .46]. Examination of the average ratings for opioid-related change recommendations indicated th at such decisions were signifi cantly more likely to be made for virtual patients who we re African-American [ t (46) = -2.79, p < .01, d = .68], female [ t (46) = -2.29, p < .05, d = .52], and displaying high expressivity [ t (46) = -5.57, p < .001, d = 1.20]. Additionally, a trend was observed wherei n older patients were more likely to have such recommendations made on their be half relative to younger patients [ t (46) = -1.66, p = .10, d = .43]. Self-reported Cue Utilization At the conclusion of the study, participants were asked to reflect back on their experiences during the clinical scenario por tion. Specifically, they were aske d what information they used when formulating their assessment and treatment ra tings for the virtual patients. Responses were provided in an open-ended format. Several themes (not mutually-exclusi ve) were identified upon inspection of these responses. Thirty-three nurses indicated that they used the facial expressions of the virtual patients when maki ng ratings. Vital sign information ( n = 28) and patient movement ( n = 10) were the next most frequently endor sed themes. Eight nurses stated that they incorporated text-based information about the gene ral clinical scenario, and 7 stated that they used text-based pain-specific information from the clinical scenario to inform their decisions. Five nurses reported using general non-verbal information, but did not specify further. Finally, 3

PAGE 59

59 nurses indicated that they relied on their clinic al experience to make assessment and treatment decisions regarding the virtual patients. Knowledge of Study Hypotheses and Cue Utilization Because task transparency and socially desira ble responding are highly relevant issues with analogue designs, at the conclusion of the st udy, participants were asked to guess at the hypotheses of interest. Responses to this open-ende d inquiry were examined for indication that a given nurse was aware of any of the hypotheses c oncerning patient cues of sex, race, and age. Targeted analyses were then conducted to de termine the influence of such awareness on assessment and treatment ratings. Due to the high volume of analyses th at could be conducted, and the consequent inflation of Type I error abse nt specific hypotheses, analyses were confined to pain intensity assessment and opioid treatment ratings. These domains were chosen because they were of most interest and relevance to c linical practice. Of the 46 nurses who responded to the query, 11 did not give any indication that th ey were aware of the hypot heses of interest. The remaining 35 gave some indication a liberal criterion was used in judging these responses of knowledge of the pertinent hypothese s. It should be noted that few of these nurses indicated awareness of all the study hypotheses. Results of Chi-square analyses co mparing these groups in their cue utilization indicated no differences in the directional weighting of patient demographic (sex: (2) = 1.14, p > .05; race: (2) = .51, p > .05; age: (1) = 1.40, p > .05) or facial expression ( (1) = .48, p > .05) cues for pain intensity policies. Similarly, no group differences were found in weightings of demographic (sex: (2) = .73, p > .05; race: (2) = .95, p > .05; age: (1) = 3.08, p > .05) or facial expression ( (1) = 1.72, p > .05) cues for opioid treatment policies.

PAGE 60

60 Exploratory Group Analyses The following exploratory analyses were conducted to determine whether putatively relevant participant characteristics were related to overall pain assessment and treatment decision policies and contextual cue ut ilization. Given that no specif ic hypotheses were articulated concerning these relationships, the results pres ented below should be in terpreted with caution. Participant Characteristics and Overall Decision Policies Participants were grouped according to the signi ficance of their overall policies. At each of the eight assessment and treatment decisions, nurses who had a policy that was significant ( p < .05) or approached significance ( p < .1) comprised one group; those with non-significant ( p > .1) policies were included in a separate grou p. Nurses with invarian t ratings were excluded. Table 3-6 presents the number of participan ts in each group. Group comparisons were then conducted to test for differences in pertinent part icipant demographic and pr ofessional variables. Table 3-6. Number of participants with significant overall policies Policy Significant Not significant Invariant Pain intensity assessment 39 14 1 Pain unpleasantness assessment 37 16 1 Positive mood assessment 25 25 4 Negative mood assessment 38 15 1 Non-opioid treatment 15 35 4 Opioid treatment 27 25 2 Recommendation: non-opioid 18 32 4 Recommendation: opioid 22 30 2 Participant Education and Overall Decision Policies Chi-square analyses were employed to test for differences in nur sing degree status and overall policy significance. Results i ndicated no significant differences ( p > .05) between nurses who were trained at the Associate, Bachelor, an d Master level in terms of their overall policy significance across the 8 ratings.

PAGE 61

61 Participant Professional Experien ce and Overall Decision Policies Comparisons between participants with si gnificant and non-significant overall policies indicated group differences in years of professional experien ce for policies involving opioid treatment [ F (1,50) = 5.83, p < .05, p = .10] and recommendation for change in opioid medication [ F (1,50) = 4.55, p < .05, p = .08]. Examination of group me ans revealed that nurses with a significant opioid treatment policy ha d fewer years of prof essional experience ( M = 11.07) than those with a non-sig nificant opioid policy ( M = 17.84). Similarly, nurses with a significant policy for recommendation of a change in opioid medication ( M = 10.27) had fewer years of professional experience than those with a non-significant policy ( M = 16.40) in this domain. No other significant differences in professional experience emerged at the level of overall policy. Participant Characteristics and Cue Utilization A similar grouping scheme as that used for overall policy was employed at the cue level. However, due to the large number of analyses required to make group comparisons across each of the four cues at each of the eight decision policies in ad dition to the absence of specific hypotheses concerning such comparisons an additional grouping method was applied. The virtual patient cues of age, r ace, and sex were combined to co mprise a demographics cue; the pain expression cue was unchanged. Nurses were then grouped according to whether they used a demographics cue in their various decision polic ies. At each decision policy, nurses who had at least one demographic cue coefficient (age, r ace, sex) that was significant or approached significance were grouped together. In the followi ng analyses, these nurses were compared to those who did not use patient demographic cues in their judgment policies. A similar process was employed at the level of pain expression cue: nurs es with a significant pain expression cue were compared to those with a non-significant pain expression cue. Participants with invariant

PAGE 62

62 responding were excluded from group analyses. Ta ble 3-7 contains the distribution of nurses across the various groups. Participant Sex and Cue Utilization Previous research suggests that the sex of the observer may interact with the sex of the individual being observed to influence pain -related ratings (Robinson & Wise, 2003). Thus, comparisons were made to determine the presence of sex differences in th e utilization of virtual patient sex cue, as well as averaged ratings of pain assessment and pharmacologic treatment. Results of chi-square analyses i ndicated that male and female nur ses did not differe ntially weight patient sex in their decision policie s about pain intensity assessment ( (2) = .92, p > .05), pain unpleasantness assessment ( (2) = 3.47, p > .05), non-opioid treatment ( (2) = .40, p > .05), or opioid treatment ( (2) = .67, p > .05). ANOVA results were also non-significant; in the aggregate, male and female nurses did not provide different pain intensity [ F (1,52) = 1.20, p > .05, p = .02] or unpleasantness [ F (1,52) = 1.67, p > .05, p = .03] assessment ratings, nor non-opioid [ F (1,52) = 1.52, p > .05, p = .03] or opioid [ F (1,52) = .40, p > .05, p = .01] treatment ratings for male and female virtual pa tients. When considered with the findings above in the Within-Cue Comparisons section, these results suggest that although assessment and treatment ratings differ based on the sex of the pa tient, these biases are equivalently shared by male and female nurses. Participant Education and Cue Utilization Comparisons in cue utilization were made acr oss degree status (Associate, Bachelor, and Master) to determine if educational achievement interacted with use of contextual demographic and pain expression information. Significant differences were found in pain cue utilization for judgments about pain intensity ( (2) = 6.51, p < .05). Ninety-three percent (14/15) and 82%

PAGE 63

63Table 3-7. Participant use of demogr aphic and pain expression cues Demographic cues Pain expression cue Policy Significant Not significant Invariant Significant Not significant Invariant Pain intensity assessment 27 26 1 41 12 1 Pain unpleasantness assessment 29 24 1 40 13 1 Positive mood assessment 25 24 5 32 17 5 Negative mood assessment 26 27 1 37 16 1 Non-opioid treatment 15 35 4 19 31 4 Opioid treatment 27 24 3 28 23 3 Recommendation: non-opioid 16 34 4 19 31 4 Recommendation: opioid 20 32 2 25 27 2 Note: Values represent number of participants in each category.

PAGE 64

64 (18/22) of nurses with a Mast ers and Associates Degree, respectively, had a significant regression coefficient for pain expression, wherea s only 56% (9/16) of th ose with a Bachelors Degree significantly weighted pa in expression in their policies for pain intensity assessment. Similar results were obtained fo r pain unpleasantn ess assessment ( (2) = 5.82, p = .055), such that 93% of Masters-tra ined, 77% of Associates-trained, an d 56% of Bachelors-trained nurses had a significant pain un pleasantness policy in which the pain expression cue played a prominent role. All other degree-based comparisons rega rding utilization of demographic and pain expression cues were non-significant ( p > .05). Participant Professional Experience and Cue Utilization Results of ANOVAs comparing nurses who did and did not have significant demographic cue coefficients indicated no differences in ye ars of professional expe rience between these two groups. When similar comparisons were made re garding pain expressi on cue utilization, two significant differences emerged. In the context of opioid treatment policies, nurses with a significant pain expression cue ( M = 10.54 years) had less pr ofessional experience [ F (1,49) = 7.93, p < .01, p = .14] than nurses with a non-si gnificant pain expression cue ( M = 18.30 years). A similar result was found for opioid-related recomme ndation policies, such that participants who used pain expression as a significant cue ( M = 10.76) had less professional experience [ F (1,50) = 4.24, p < .05, p = .08] than those who did not ( M = 16.63). No significant differences in professional experience emerged for the other decision policies.

PAGE 65

65 CHAPTER 4 DISCUSSION Issues related to the assessment and treatment of pain have received increased theoretical and empirical attention over the past several ye ars. Taken in its entirety, this body of work indicates that clinical pain is frequently in adequately assessed and under-treated (e.g., Cousins, 1994; Manyande, 1996; Thomas et al., 1998; WHO, 1986). Patient characteris tics in particular, sex, race/ethnicity, and age have been identified as a potential source of these deficiencies (e.g., Anderson et al., 2000; Cleeland et al., 1994 ; Horgas & Elliott, 2004; McDonald, 1994; Ng et al., 1996b; Oberle et al ., 1990; Robinson & Wise, 2003). Unfortunately, methodological limitations of previous investigations in th is field place considerable constraints on the conclusions that may be drawn from them. The current study sought to ad dress several of these limitations through implementation of an innovative research design and methodology. Additionally, this investigation was structured to pr ovide a more detailed an alysis of the clinical decision making process in order to better characterize the extent to which patient characteristics influence provider decision-making a bout pain. Overall, results indi cated that the virtual patient technology and lens model methodology were successf ul in capturing and detailing the painrelated decision policies of nurse participants. Although replication of th is success is certainly needed, the current investigation illustrates an alternative and promising approach by which to continue the study of me dical decision-making. Results of idiographic analyses of pain a ssessment ratings indicated that approximately 70% of nurses had significant deci sion policies in this domain. Stat ed differently, the contextual information provided in the clinical scenarios wa s sufficiently weighted by the majority of nurses to result in a reliable decision product. It would appear then, that despite the constraints on cue number that lens model designs impose (see disc ussion below), highly relevant information was

PAGE 66

66 provided for clinical decisions about pain assessment. Although negative mood assessments were similarly reliable, ratings of positive mood were less consistent. This is to be expected given the clinical context. Assessment of negative mood in a patient apparently even a virtual one experiencing acute, post-operative pain should be less subject to error than assessment of positive mood in that same patient, given that negative mood is more consistent with the experience of pain (Robinson & Riley, 1998) and lik ely to be more frequently encountered by healthcare professionals. Analyses of pain treatment policies indicat ed that almost twice as many nurses had significant opioid than non-opioid policies. Fo r these particular hea lthcare providers, the information contained in the clinical scenarios was apparently more serviceable for decisions about the use of opioid medications than non-opioid medications. It is also possible that these results are due to nurses greater familiarity wi th opioid medications in an acute pain context and/or the relative paucity of guidelines specific to non-opioid me dications. In the final decision domain recommendation for a change in medication a significant non-opioid recommendation policy was found in one-third of nurses, whereas 41% had a significant overall opioid policy. These data suggest that when compar ed with other decision domains, particularly the assessment policies, decisions about medication recommendations were less influenced by the contextual cues available to study participants This is not surprising when one considers that the other decision domains were likely to be perc eived as more precise relative to this domain (see below). If so, the decision policies of individual nurses w ould be expected to be less consistent, resulting in fewer signi ficant overall policies. There is al so likely to be considerable differences among nurses in terms of their comfort in making medication-related recommendations. These differences may be a pr oduct of the considerable variability in the

PAGE 67

67 medical cultures within which individual nurses practice (Casanova et al., 2007; Irvine et al., 2000; Pollard, 2003). The consistency of assessment policies pa in and mood relative to both medication and recommendation treatment domains also suggest s that fewer additional cues are needed by nurses when making decisions abou t the experience of pa in and mood in their patients. This is supported by the finding that the greatest number of available cues were used for pain and mood assessment policies. In contrast, the contextual cu es contained in the clin ical scenarios were not sufficient, and may not have even been necessa ry, to produce a reliable decision product in a large number of nurses in this study, particul arly in the non-opioid treatment and both recommendation domains. Future research is need ed to further identify those cues that are particularly germane to these treatment decisions. A major innovation of the curr ent methodology lies in its ability to capture the decisionmaking process as well. Analyses at this level indicated that pa tient demographic cues played a significant role in many nurses assessments of pain intensity and unpleasantness. The vast majority of those who used sex as a significant cu e tended to assign higher pa in ratings to female patients. In addition to its stat istical significance, this cue acc ounted for a rather substantial amount (up to 14%) of the total va riance in the pain assessment d ecision policies of some nurses. When ratings were averaged across participants for more traditional nomothetic analyses, female videos received significantly higher ratings th an male videos, even after controlling for stereotypic beliefs a bout gender and pain. These results are interesting when considered in light of the liter ature that indicates females are at greater risk of having their pain under-assessed in the c linical context (Anderson et al., 2000; Cleeland et al., 1994), whereas in the experimental setting, females are judged to be

PAGE 68

68 experiencing greater pain than males (Robi nson & Wise, 2003). The current study differs in many respects from these previous investigatio ns. An atypical and innovative hybrid design was employed in that participants made assessment rati ngs of a clinical nature in an experimental context. Differences in study participants are al so noteworthy. This st udy was largely comprised of female nurses, whereas the af orementioned clinical investigati ons consisted mainly of male physicians; participants in the Robinson and Wi se study were primarily college undergraduates with a roughly equal proportion of males and fema les. In the current study, no differences were found between male and female nurses utilization of patient sex cue, or in their averaged ratings for male and female videos. Although lack of st atistical power should be considered given the small proportion of male nurses, the corresponding effect sizes were small, suggesting that any differences that do exist are likely to be of little consequence. These across-study differences make a clean synthesis of this literature diffi cult. Although the current re sults are supportive of the hypothesis that, relative to males, females are viewed by others as having greater pain, future investigations are needed to furt her elucidate the role of patien t and provider sex in decisionmaking about pain assessment. Greater variabil ity in provider sex and professional role are particularly critical. The virtual patient race cue also emerged as a prominent contributor to many nurses pain assessment policies. Most nurses who significantl y weighted patient race assigned higher pain ratings to African-American patients, and the magnitude of this cues effect was similar to that of patient sex. At the nomothetic level, African-A merican patients receiv ed significantly higher ratings of pain intensity and unpleasantness than their Caucasian counterparts. These findings were surprising and counter to a priori hypotheses. The relevant litera ture on this topic is small and mixed, with some evidence that African-America n patients are at greater risk of having their

PAGE 69

69 pain underestimated relative to Caucasians (C leeland et al., 1997) and some reports of no racial/ethnic differences in pain assessment (Todd et al., 2000). To the authors knowledge, this is the first study to find that African-American patients received higher pain ratings than Caucasians. The same unique char acteristics of the current study not ed above also hold relevance here in terms of understanding th ese disparate findings. Methodol ogical differences between the studies that have been conducted on this topic are significant and, when taken together with their small overall number, place considerable constraints on the drawing of overarching conclusions. It is, at present, unclear w hy patients of different racial/e thnic backgrounds were judged to be experiencing different levels of pain in th e current study despite the fact that contextual information particularly pain expression was standardized across patient. Perhaps study participants were sensitive to scientific and medi a reports of racial/ethni c disparities in medical care and took particular caution no t to underestimate relative to Caucasian patients the pain experience of African-American virtual patients Perhaps female nurses are less likely to hold and/or act on biases concerning minority populations. The development of multicultural competence is a recurrent theme in the nursing literature and highly emphasized among nursing education programs (Fitzpatr ick, 2007; Hughes, & Hood, 2007; Lipson, & DeSantis, 2007; Robinson, 2000; Underwood, 2006). It is certainly possi ble that the effects of such attention in the nursing field were transferred to pain asse ssment ratings in the cu rrent study. It is also difficult to ascertain the meaning of these racial/ethnic differences in pain assessment ratings. In judging African-American virtual patients as having greater pain relative to Caucasian patients, did nurses discount the pain expe rience of Caucasian patients? Di d they view African-Americans as being less able to tolerate similar pain expe riences? These are intrigui ng questions that could not be addressed by the current study. Regardless, that significant differences in pain assessment

PAGE 70

70 at both the idiographic and nomothetic levels emerged is cause for concern and continued research. The final patient demographic cue age also played a significant role in the pain assessment of many nurses. In fact, almost one -quarter of nurses weighted age in their assessment decisions, with all but one of these pr oviding higher pain rating s for elderly patients. The greatest amount of variance in these decision policies account ed for by age (over 25%) even exceeded that of patient sex a nd race. Averaged ratings across study participants also showed that older videos received significantly highe r ratings than younger videos. The literature documenting the under-assessment of pain in olde r individuals is robus t (Cohen-Mansfield & Lipson, 2002a, 2002b; Cook et al., 1999; Ferrell, 1995; Ferrell, 1996; Gloth, 2000; Horgas & Elliott, 2004; Kaasalainen et al., 1998; Sengstake n & King, 1993); thus the current results were not expected. However, much of this literature has focused on the comorbid medical conditions particularly dementia that are more prevalent in older populations and li kely to complicate the assessment of pain in these patients. The current clinical scenario was standardized, such that older and younger patients presente d with equivalent conditions. That older patients were judged to be experiencing greater pain than younger patie nts is, therefore, particularly intriguing. As discussed above in the findings regarding race, th e precise implications of these results are not clear. Was the pain experience of older patients over-estimated? Or was the pain experience of younger patients under-estimated? Without patient self-report information which, for obvious reasons, was not possible in this study thes e questions remain unanswered. They do, however, provide direction for future research efforts in this domain. The inclusion of relevant comorbid conditions as an additional cue in future investiga tions would also likely prove fruitful in further elucidating the role of patient age in the assessment of pain.

PAGE 71

71 The pain expression cue emerged as a highly important contributor to assessment ratings; approximately 70% of study partic ipants reliably used this cue when making decisions about pain intensity and unpleasantness. All of these nurses judged patients disp laying high levels of pain expression to be experiencing greater levels of pain. Further, up to 79% of the variance in these ratings were accounted for by patient expressi on. Consistent with these findings, results of nomothetic analyses indicated large differences in average pain intensity and unpleasantness ratings of high vs. low pain expression videos. The methodological implications of these results are encouraging. Although the faci al manipulations of virtual patients were guided by the FACS and, thus, were expected to closely approximate the empirically-validated pain expression (Craig et al., 1992; Prkachin, 1992b), the current findings regarding partic ipant use of this cue provide further validation of the manipulatio n. As a clinical matter, it is reassuring that pain expression was the most salient cue in pain assessment polic ies. This finding is consistent with previous work highlighting the considerab le influence of nonverbal expre ssions on observers ratings of pain in others (Ahles et al., 1990; Hale & Hadj istavropoulos, 1997). In fact, observers have been found to rely more on such nonverbal expression s than even self-report (Craig, 1992; Poole & Craig, 1992). Replication of these findings with virtual patient methodology would be an interesting future endeavor. Turning to policies regarding the treatment of pain, when these ratings were submitted to idiographic analyses virtual pa tient sex played a relatively mi nor role in decisions about nonopioid treatment, with only two nurses significantly weighting this cue. Opioid-related decisions, on the other hand, were more frequently influe nced by patient sex. With one exception, when patient sex served as a significant cue, female patients received higher ratings and, thus, were more likely to be administered medication for pa in. Although fewer nurses reliably used this cue

PAGE 72

72 for treatment decisions, the policies of those that did were even more affected by sex than in the assessment domain. At the nomothetic level, sex differences were found only for opioid decisions, with females, again, more likely to receive such treatment. That patient sex had an influence on the pa in-related treatment decisions for many nurses was not surprising. It was surpri sing, however, and counter to st udy hypotheses, that when sex was a significant cue female patients were more likely to receive pain medication than males. There is substantial evidence that females r eceive sub-optimal pharmacologic management of their pain relative to males (Beyer et al., 1983; Calderone, 1990; Cleel and et al., 1994; Cohen, 1980; Faherty & Grier, 1984; Mc Donald, 1994; McDonald & Bridge 1991). It is important to note, however, that sex differences are not always found (Bartfield et al., 1997; Turk & Okifuji, 1997, 1999). Differences have even been found in the reverse direction, with early studies indicating that females were the recipients of more aggressive tr eatment (Bond & Pilowsky, 1966; Pilowsky & Bond, 1969). The current results are consistent with this early literature. Bond (1971) interpreted his findings in the context of a cu lturally-sanctioned belief system wherein males are expected to be more tolerant of pain than females. Although intuitive, this conclusion was speculative absent any additional, supportive data. The inclusion of a measure of gender-role stereotypic beliefs about pain in the current study provided an oppor tunity to directly test this hypothesis. Results indicated no significant rela tionship between these beliefs and treatment ratings. Thus, it would appear that, as measured by the GREP, providers pain-related stereotypic beliefs do not explain the sex difference in their treatment of pain. What, then, is driving these results? Potential explanations lie in the sex and role of the pr ovider. As noted above, previous investigations have la rgely included male physicians. The fa ct that the current study enrolled only nurses, most of whom were female, could explain the di sparate findings. Although the

PAGE 73

73 current study attempted to address the provider sex issue through targeted recr uitment, this effort was likely not successful in securing enough ma le nurses for adequately powered sex-based comparisons in treatment policies. In regards to provider role, it is also possible that treatmentrelated sex differences are more likely to manife st in the context of medication prescription and administration primarily a physician activity a nd are less prominent in clinical activity that consists solely of medication administration, an activity that is largely the purview of nurses. This hypothesis is speculative but if true, could explain the di screpancy between the current results and those of other recent investigations. Patient race was significantly weighted by 6% and 9% of nur ses, respectively, in their nonopioid and opioid treatment decisions. With one exception in each domain, nurses were more likely to engage in these treatment practices with African-American patients than Caucasian patients. Despite their small numb er, the nurses who significantly weighted race in their policies did so to a larger degree than in the assessm ent domain. In the aggregate, a medium-sized difference was found, such that African-American patients received signi ficantly higher opioid treatment ratings than Caucasians. No race differences were found in averaged non-opioid ratings. As in the pain assessment domain, these results are in conflict with the relatively large and consistent literature demonstrating that minority individuals pain is under-treated relative to individuals of the dominant ra cial/ethnic background (Anderson et al., 2000; Bernabei et al., 1998; Cleeland et al., 1994; Cleeland et al., 1997; Ng et al., 1996a; Ng et al., 1996b; Ross, 2000; Sambamoorthi et al., 2000; Tamayo-Sarver et al., 2003b; Todd et al., 1993, 2000). Several explanations have been articulated to account fo r these findings. In addition to frank racism on the part of providers, a widely he ld explanation is that patient ra ce serves as a proxy for the true operating variables that drive these treatment differences. These include factors such as

PAGE 74

74 differential communication, SES, and access to health care. Aside from the cues of interest that were systematically manipulated across virtual patie nts, all other factors in the current clinical scenarios were held constant. The findings that African-American patien ts did not receive suboptimal treatment compared to Caucasian patie nts and, in fact, were the recipients of more aggressive pharmacologic care in many instances are more in line with a race-as-proxy explanation than one based on provider racism. Futu re investigations that include cues such as patient SES are needed to better address these important issues. The speculative hypotheses articulated above con cerning provider sex and role may also be extended here. In fact, there is evidence that fema les harbor less racial/ethnic biases than males (Bier, 1990; Johnson, & Marini, 1998; Qualls, Cox, & Schehr, 1992; Schuman, Steeh, & Bobo, 1997; Wuensch, Campbell, Kesler, & Moore, 2002) The findings observed in the present study could, thus, be attributable in part to the overwhelming majority of female participants. It will be interesting to revisit this issue in the future when a sufficient number of male providers have been enrolled. An additional factor that may bear on the differences between the current results and those of previous studies concerns th e manner in which treatment decisions were operationalized. The current study examined nurses likelihood of administering a given medication, whereas previous studies have largely focused on the amount of medication administered. This seemingly subtle difference c ould, in fact, be quite im portant. Future studies would need to include both likelihood and amount ratings in regards to medication-related decisions to further clarify this issue. The context in which the pain is occurri ng may also have implications for these differences. The post-surgical scenario employed in the pres ent investigation is rather straightforward and, thus, may be less likely to elicit non-medical influenc es (e.g., racial biases)

PAGE 75

75 on providers treatment-related decisions than mo re ambiguous scenarios such as migraine or sickle cell crises (Tamayo-Sarver et al., 2003b). It is also possible that the type of methodology used to investigate these issues is of importa nce. Despite its novel innovations, the current study is still properly classified as a vignette-based approach. Two ot her recent vignette studies found that race of hypothetical patients did not influe nce analgesic practice among medical providers (Campbell, 2002; Tamayo-Sarver et al., 2003a). Perh aps these designs provide sufficient shelter from the time and financial pressures of real me dical practice that may make biased decisions more likely. To address this hypothesis, future modifications to the current approach could include time constraints, such that assessment a nd treatment decisions must be made within a circumscribed period of time. When patient age was analyzed at the individual level, it emerged as a significant cue in the non-opioid treatment policie s of 11% of the study sample. Seventeen percent had opioid policies in which age played a significant role The magnitude of these effects was roughly equivalent to pain assessment policies. The dire ction of the age effect was somewhat mixed for non-opioid decisions, the effect of which was se en when ratings were aggregated and no significant differences emerged between younger a nd older patients. More consistent results were found for opioid treatment; the majority of nurses were more likely to administer opioid medication to older patients relative to younger on es. This greater consistency was borne out at the group level, as a moderate-to-large age difference was found. The relevant empirical literature is small a nd, thus, should be approached with caution. Nevertheless, in contrast to the current da ta, Oberle and colleagues (1990) found that postsurgical elderly patients received less medication for pain than th eir younger counterparts. In a vignette-based study, Campbell (2002) also found that hypothetical elderly patients were at

PAGE 76

76 greater risk of being under-medicated for acute pain relative to younger ones. Returning to the current results, not only is it surprising that elderly patients often received higher treatment ratings, that this was particularly so at least in terms of the directional consistency of the effect for opioid medication is remark able. It is well-documented th at providers are increasingly reluctant to administer this class of medicati ons, even in the most severe cases (Portenoy, 1996). Thus, it would be expected that age-related practice differences el derly receiving more conservative treatment as assessed in the current study would be more likely to manifest for decisions about opioids. Since, re lative to their younger counterparts, older individuals are more likely to present with a larger array of co morbid medical conditions (Anderson, & Horvath, 2002; Hoffman, Rice, & Sung, 1996; Wolff, Starfi eld, & Anderson, 2002), the current findings may be a consequence of the standardization of clinical presentation across virtual patients. These results tentativel y suggest that when older and younge r patients present with similar conditions both number and type older patients, by and large, receive equivale nt pain-related pharmacologic treatment and may even be the reci pients of more aggressive treatment by some nurses. The level of pain expression displayed by virtual patients significantly influenced treatment decisions regarding non-opioid and opioid medications in 24% and 46% of study participants, respectively. Interestingly, the dir ection of this effect was approximately equally split for non-opioid decisions and entirely consistent for opioid decisions, with high expression patients receiving higher ratings. As with pain assessment ratings, this cue had the largest absolute effect on nurses overall policies in the treatment domain. When analyzed nomothetically, not surp risingly given the roughly equal sp lit, no difference emerged in average non-opioid ratings between patien ts displaying low and high pain expressions. In contrast, a

PAGE 77

77 large difference was found for opioid treatment ratings; high expression patients were more likely to be administered medication than low expression patients. The natural question that arises from these sets of findings is, relative to their directional count erparts, did those nurses who were less likely to administer non-opioid medication to high expression patients provide higher opioid medication ratings for these patients. Follo w-up analyses indicated that this was not the case; there were no differences in opioi d ratings for high or low expression patients between the two groups who used the pain expr ession cue in opposite directions for non-opioid treatment decisions. What then accounts for these individual differences in cue use? Unfortunately, that question must remain una nswered at present, but is an important consideration for future investigations. The utilization of patient demographic cues in decision-making about pain assessment and treatment, even if found in only a small clus ter of nurses, is highly significant when one considers several features of cu rrent nursing practice. The first c oncerns nurse-to-patient ratios. California is currently the only state to set a ratio limit for inpatient hospital units (Buchan, 2005). Medical/surgical units the mo st relevant to the current discussion are prohibited from exceeding a ratio of 1:5. If one adopts this as a conservative estimate of the average nurse-topatient ratio across the country, then a given full-time nurse ha s the opportunity to assess and treat the pain of hundreds of patie nts each year and thousands of patients in a career. In this context, even a small propensity to utilize pati ent demographic characteristics in pain-related decision-making is of considerab le importance. A second releva nt feature of current nursing practice is related to training and modeling. Th e precepting of new nurses is a very important aspect of professional nursing practice (A lspach, 2000; Hardy, & Smith, 2001; Shamian, & Inhaber, 1985; Speers, Strzyzewski, & Ziolkowsk i, 2004). If, in this capacity, a veteran nurse

PAGE 78

78 conveys the message the self-re ported cue utilization data from the current study suggest that this is likely to be an implicit process that patient demographic char acteristics are to be considered when making pain-related decisions, the consequences could be dramatic. Not only would the precepted nurses be more likely to engage in similar practices, but if they served as preceptors in the future, the transmission of such in appropriate clinical behavior is furthered still. Similar dissemination is likely to also occur outsi de of formal training in the form of modeling. Thus, any one nurse with a given propensity to weight patient dem ographic cues in the assessment and/or treatment of pain could have a far-reaching influence on clinical practice. Policies regarding the likelihood of recommendi ng a change in medication to obtain better pain control were also influenced to some degree by contextual cues. Patient sex played a relatively minor role in the decision-making pro cess for most nurses; however, for the few that did use this cue in a reliable manner, the eff ect was quite substantial. When ratings were combined for group analyses, female patients received significantly higher ratings for both medication classes. Race was a somewhat larger fact or in this context. At the idiographic level, the direction of effect was approximately equa l for non-opioid medications, which manifested in a non-significant within-cue di fference in aggregated ratin gs for African-American and Caucasian patients. In contrast, the results were entirely consistent for opioid recommendations. All 4 nurses with a significant race cue made higher likelihood ratings on behalf of AfricanAmerican patients. Although these nurses represen ted less than 10% of th e entire sample, their use of virtual patient race was enough to produc e a significant and re latively substantial aggregate difference between ratings for African -Americans and Caucasians. Virtual patient age had a minor impact on non-opioid recommendation decisions but a somewhat larger one for opioid decisions. More nurses gave higher non-opioid recommendation ratings to younger

PAGE 79

79 patients than older ones; however the reverse was true for opio id medication. When these data were collapsed for group analyses, no age diffe rences emerged for either medication. Finally, and consistent with previous resu lts, virtual patient pain expre ssion played a relatively large and consistent role in recommendati on decisions. Most nurses who used this cue in a consistent and significant manner were more likely to make r ecommendations on behalf of patients displaying a high pain face. It is not surprisi ng, then, that similar significant within-cue differences for pain expression emerged at the nomothetic level. A precise elucidation of the above results is difficult at this time. Perhaps the most pressing interpretive challenge is that the intent of the ra tings is not known. It is likely that while some nurses made ratings with the objective of recommending a change in medication type others were pursuing a change in medication dosage ; still others could have intended something different altogether. Without this level of informational detail, furt her interpretation of these data is exceedingly speculative and, thus, imprudent. Future studies could address these issues by having nurses indicate thei r intent and likelihood. Although not a primary target of the current inve stigation, the impact of contextual cues on mood assessment ratings was also determined. Virtual patient sex emerged as a relatively insignificant cue in the assessment of positive mood but a more influential constituent of policies involving negative mood assessment. When virtual patient sex cue was weighted by participants, males tended to be viewed as experiencing a more favorable mood (less negative, more positive), whereas the opposite (more negative, less positive) was true for fe males. Research supports the existence of strong, sex-specific socialization pres sures that bear on the judgment of anothers emotional experience. For example, Gaelic k, Bodenhausen, and Wyer (1985) found that expressions of happiness and anger by women were perceived as more and less intense,

PAGE 80

80 respectively, than expressions of equivalent intensity displa yed by men. Even young children are affected by these stereotypes (Haugh, Hoffman, & Cowan, 1980). An interesting reversal of the stereotype was reported by Hess, Adams, and Kleck (2004), in that women were rated as more angry and men as more happy. The current result s are consistent with this study by Hess and colleagues, and although both of these findings coul d be spurious merely the result of chance they can also be meaningfully interpreted in the context of the Sh ifting Standards Model (Biernat, Manis, & Nelson 1991). Briefly, this model posits that subjective judgments of individuals from different social groups may fail to elicit the ster eotyped expectations of judges, because they invite the use of different evaluative standards. As applied to the current study, males and females are generally expected to display more negatively and positively valenced emotions, respectively. Consequently, a given ne gative emotional expression an expression of greater pain in the current study is perceived as more negative when displayed by a female than a male. Conversely, a given positive emotion is rated as more positive when expressed by a male than a female. This interpretation is tentativ e but provides an intri guing basis for future investigations of the pain-mood nexus. As with virtual patient sex, there was directiona l variability in individu al nurses use of the race cue. Nevertheless, more nurses were inclin ed to view African-Americans as experiencing less favorable mood than Caucasia ns. Cross-cultural recognition a nd categorization of emotional expressions has received considerable empiri cal attention (Dickey & Knower, 1941; Ekman & Friesen, 1972; Fridlund, Ekman, & Oster, 1987; Izard, 1968; Matsumoto, 1987; Schimmack, 1996; Shioiri et al., 1999). However, this research has been overwhelmingly asymmetrical in its focus on the race/ethnicity of th e observer to the conspicuous excl usion of the race/ethnicity of the observed individual. Given the lack of va riability in the raci al/ethnic background of

PAGE 81

81 participants, the current study focu sed exclusively on the characteris tics of the observed patient. Consequently, the voluminous literature on cross-cu ltural issues in the expression and perception of emotional states provides little interpretative guidance for the current results. Of all the demographic variable s, age demonstrated the largest and most consistent impact on mood assessments, with approx imately one-quarter of partic ipants using this cue in a significant manner. With one exception, these nu rses rated younger patients as experiencing more favorable mood than their older counterparts; the directional consistenc y of this effect was borne out in group analyses of average ratings. A small literatu re exists on the perception of elderly individuals emotional expressions. In contrast to the current findings, Borod and colleagues (2004) found no difference in intensity ratings of positive and negative emotions expressed by individuals of varying age gr oups. A similar finding was reported by Moreno, Borod, Welkowitz, & Alpert (1993) in which the age of the expressor did not influence the perceptual accuracy scores of raters. In contrast, Malatesta, Fiore, & Messina (1987) found that the emotional expressions of older subjects were not perceived with clarity. Although this study lacked an adequate, younger comparison group, it doe s suggest that older persons are at risk for misattribution of their emotional displays. Give n the substantial differences in methodology and purpose, the relevance of this literature to th e current study is unclear. Additional research is needed to clarify the effect of patient age a nd other demographic characteristics for that matter on providers emotional assessment in a clinical pain context, a topic that has not received adequate attention to date. The final cue of interest pain expression again emerge d as a strong and consistent contributor to decision policies in the domain of mood. Fortyfour percent and 63% of the sample manifested a significant pain expression policy in assessments of positive and negative

PAGE 82

82 mood, respectively. All rated highly expressive patients as experien cing less favorable mood. These results are not surprising considering the ov erlap in core action units namely AUs 4 and 7, which correspond to movements of the brow and eyes, respectively of various negative emotional states (e.g., anger, fear, sadness) a nd pain. From a conceptual point of view, concomitant negative affect is expected with the experience of pain. Indeed, the IASP (1994) defines pain as "an unpleasant sensory and emotional experience associated with actual or potential tissue damage or describe d in terms of such damage" (italics added). Thus, even in the absence of explicit mood-related informati on, one would expect th e assessment of mood particularly negative mood in a clinical pain context to be a relativ ely reliable process and amenable to statistical modeling. An interesting issue that emerges from the di scussions above concerns the integration of idiographic and nomothetic results. The vast majo rity of scientific investigations involving human participants are nomothetic; i.e., data is collected from individu als and aggregated for group analyses. The current study highlights a majo r limitation of a strictly nomothetic approach, namely, that it provides an incomplete persp ective on the phenomenon of interest. To take an example from the current data, if one limited th e analysis of patient race and opioid treatment decisions to solely a nomothetic approach, the conclusion would consist only of the following: African-American patients received significan tly higher opioid treatment ratings than Caucasians, and this difference was of a medi um-size effect. If, however, an idiographic perspective was added, the conclusion would be much different. Results of an idiographic analytic approach indicated that, far from being a ubiquitous phenomenon, such race-based differences in opioid treatment were only present in 9% of study participants. And of these 9%, one nurses use of the race cue even operated in the opposite direction. The implications of these

PAGE 83

83 results are very different. A strictly nomothe tic approach could possibly lead to calls for sweeping intervention efforts aimed at addressing this problem of differential pain treatment practices among racial groups. On the other hand, idiographic results suggest a more nuanced approach, one that involves the identification and targeting of those individuals who produced discrepant ratings across race. Th e current study was primarily focu sed on the contextual cues of interest and, consequently, did not secure sufficient variability in key provider characteristics to address the individual differences question. Ne vertheless, results we re suggestive of the influence of provider education and experience on pain-related decisions. Continued systematic efforts to include providers of diverse personal and professional characteristics are needed to further this next step in the literature, namel y, the identification of which providers are most likely to weight patient demographic cues in their clinical decisionmaking about pain. The idiographic-nomothetic distincti on highlighted in the current study also provides an alternative lens through which to view the existing literature in this field, as well as a unique methodological approach by which to conti nue this line of research. A significant limitation of lens model designs is the necessity to restrict the number of contextual cues available to st udy participants. The purpose of this restriction is primarily twofold. First, the cognitive capacity of human participants is limite d; only 7 2 independent pieces of information (i.e., cues) can be processed at any one time (Miller, 1956). Thus, Cooksey (1996) recommends that no greater than nine cues be included in a study. Inclus ion of a greater number of cues increases the probability of inconsistent responding, inconsistent cu e use, and/or ignoring of cues. A second reason for limiting the number of available cues has to do with the number of scenarios presented. As the number of cues incr eases, the number of scenarios needed also increases, but at an exponentia l rate. The mere addition of a few more cues in a given study

PAGE 84

84 could easily increase the necessary number of clini cal scenarios to a practically infeasible total. The consequence of such cue restriction is that many potentially important cues to the decision process of interest must be excluded from a ny one study. One way of determining the relevance of cues included in a particular study, and of th e need in future work to incorporate additional cues, is to examine the coefficient of determination ( R ) for a given policy. In the current study, when averaged R values were compared across deci sion domains, pain assessment policies emerged at the top. Further, the greatest proporti on of study participants ha d a significant overall policy in the assessment domain. These results indi cate that the available contextual cues were highly relevant to decisions a bout pain assessment. Conversely, additional cues are apparently needed to better capture the decision making pro cess in the domains of non-opioid treatment and medication-related recommendations. A shared feature of all empirical investiga tions is the presence of limitations. Although several limitations of the current study have been noted throughout th e discussion, perhaps the most conspicuous limitations are related to its analogue nature. All an alogue studies must strongly consider their representati veness of the topic of interest as it actually exists in the world. The current study attempted to mitigate this limita tion, while still exerting rigorous experimental control, through the use of vi rtual patient technology. This innovation notwithstanding, study participants were still making clin ical decisions about a hypothetical virtual patient in a contrived experimental setting delivered vi a computer. Furthermore, the vi deos captured only the head and face of virtual patients and contained no ve rbal component. To address this issue of representativeness, feedback was elicited from nur se participants concerning several features of the study. Over 70% indicated that the virtual patient facial expressions were realistic depictions of pain. Over 90% considered th e clinical scenario to be re flective of a real post-operative

PAGE 85

85 scenario. Over 80% of participants rated the patient information as similar to that encountered in a real clinical setting. Finally, over 70% stated th at their decisions regarding the treatment of the virtual patients were similar to decisions they would make regarding a real patient. Although these responses suggest a high degree of repres entativeness, one must remain cautious when attempting to extend these findings to actual clin ical practice. A second issue concerns the face validity of the investigation and consequent potential for particip ants to respond in a socially desirable manner. Although many nur ses in the current study expresse d at least partial awareness of the hypotheses of interest, they did not differentially weight patient demographic or facial expression cues relative to thos e who did not express such awareness. This lack of a difference may be due, in part, to the fact that all nurses in this study were unaware of their use of such cues. In fact, not one nurse indicated that s/he used a patient demographic cue for any of the decision domains. In conclusion, the current study found that the patient demographic cues of sex, race, and age are significant contributors to the pain-related decision policie s of many nurses. The level of pain expressed by the virtual patient was also found to be an important factor in this context. In addition, this study demonstrated the use of i nnovative novel virtual pati ent technology and lens model methodology in the investigat ion of these highly important i ssues. Continued research is needed to address the many questions raised here in, with the goal of improving the assessment and management of all patients suffering from pain.

PAGE 86

86 APPENDIX RESULTS OF IDIOGRAPHIC REGRESSION ANALYSES Table A-1. Policies toward pain intensity assessment Participant Age Race Sex Pain R 1 -.013 .085 .366* .411* .310* 2 .189 .000 -.095 .452* .249 3 .254 .295 -.268 .282 .303* 4 .174 .253* -.016 .793** .724** 5 .491** .104 .097 .630** .658** 6 -.084 .118 .196 .174 .089 7 .507** .140 -.243* .552** .640** 8 .278 .155 .214 .545** .444** 9 .173 .381* .042 .485** .412** 10 .070 .249 -.082 .436* .264 11 -.126 .005 .170 .603** .408** 12 -.065 .157 .166 .792** .684** 13 -.098 .153 .153 .094 .065 14 .149 -.200 .353* .472** .409** 15 -.005 .099 -.049 .740** .560** 16 .088 .059 .294 .059 .101 17 .221 .099 .173 .550** .391** 18 -.121 -.178 .099 .335 .168 19 .198 .033 .160 .566** .387** 20 -.010 .332 .033 .115 .125 21 -.066 .074 .249 .629** .467** 22 .206 .077 -.060 .652** .477** 23 .264** .061 -.157 .809** .753** 24 -.158 -.058 -.050 .605** .397** 25 .244 -.201 .310* .489** .435** 26 .239 .274 .104 -.007 .143 27 .148 .167* -.164 .859** .815** 28 .318 .169 -.012 .186 .165 29 .392** .058 .315* .586** .600** 30 .179 .140 .000 .420* .228 31 .027 -.027 -.118 .027 .016 32 .204 -.058 .065 .660** .485** 33 -.042 .363* -.005 -.035 .134 34 -.079 -.280* -.040 .700** .576** 35 -.211 .105 .090 .105 .075 36 .280* .040 .249 .633** .543** 37 .029 .107 .257 .670** .528** 38 .261 .289 .219 .476** .425** 39 .080 -.080 -.064 .161 .043 40 .144 .313** .049 .729** .652**

PAGE 87

87 Table A-1. Continued Participant Age Race Sex Pain R 41 -.169 -.003 .090 .877** .805** 42 .256 .054 .112 .425* .262 43 .309 .288 .116 -.133 .209 44 .172 .136 .335** .733** .697** 45 .044 .086 -.065 .467** .232 46 .051 -.093 -.157 .469* .262 47 .296* -.070 .206 .706** .633** 48 .153 .173 .009 .792** .680** 49 .369** .252 .098 .580** .547** 50 .226 .011 -.138 .414* .242 51 ------------------------------------------------------------52 .041 .209 -.116 -.058 .062 53 .107 .000 .064 .735** .556** 54 -.056 .153 .237 .704** .578** **p<.01,*p<.05, p<.10

PAGE 88

88 Table A-2. Policies toward pa in unpleasantness assessment Participant Age Race Sex Pain R 1 .038 .022 .347* .503** .376* 2 .256 -.147 .134 .378 .248 3 .204 .016 -.376* .031 .184 4 .181 .186 -.081 .802** .718** 5 .510** .178 .216* .649** .760** 6 -.085 .061 .195 .159 .074 7 .529** .145 -.266* .523** .645** 8 .182 .259 .213 .472** .368* 9 .212 .323* .199 .552** .494** 10 .050 .192 .024 .537** .328* 11 .032 -.104 .304* .581** .441** 12 -.119 .131 .190 .778** .672** 13 .166 .103 .225 -.095 .098 14 .132 -.064 .343* .464** .354* 15 .201* .000 .081 .886** .832** 16 -.014 .072 .359* -.101 .145 17 .121 -.093 .111 .753** .602** 18 -.046 -.184 .064 .326 .146 19 .188 .033 .161 .571** .388** 20 .043 .340 -.017 .157 .143 21 -.053 .233 .348* .548** .478** 22 .209 .064 -.055 .643** .465** 23 .243* .030 -.076 .848** .785** 24 -.111 -.012 .009 .607** .381** 25 .234 -.218 .329* .484** .445** 26 .178 .260 .082 -.034 .107 27 .201* .106 -.191* .831** .779** 28 .294 .215 .115 .136 .165 29 .401** .058 .330** .586** .617** 30 .180 .173 .025 .375* .204 31 -.023 -.281 -.068 .144 .105 32 .201 -.044 .098 .638** .459** 33 -.129 .354 -.008 -.051 .145 34 -.152 -.324* -.031 .703** .623** 35 -.132 .050 -.066 -.083 .031 36 .257 .013 .234 .659** .555** 37 .089 .069 .146 .578** .368* 38 .234 .219 .234 .537** .446** 39 .286 -.051 -.139 .198 .143 40 .136 .321** .037 .732** .659** 41 -.191 .000 .124 .887** .838** 42 .203 .046 .091 .475** .277 43 .076 .181 -.133 -.181 .090

PAGE 89

89 Table A-2. Continued Participant Age Race Sex Pain R 44 .143 .113 .303** .767** .713** 45 .050 .145 -.061 .457* .236 46 .390* .034 -.074 .441* .359* 47 .292* -.085 .204 .730** .667** 48 .206 .201 -.001 .711** .588** 49 .387** .244 .120 .562** .540** 50 .130 -.152 .152 .352* .187 51 ------------------------------------------------------------52 .177 .157 -.218 .000 .104 53 .177 .032 .121 .720** .566** 54 -.108 .134 .186 .762** .644** **p<.01,*p<.05, p<.10

PAGE 90

90 Table A-3. Policies toward positive mood assessment Participant Age Race Sex Pain R 1 -.341* -.058 -.389* -.427** .453** 2 .035 .383* -.104 -.087 .166 3 .180 -.012 -.156 .337 .171 4 -.189 -.316* .032 -.516** .403** 5 -.318* -.318* .053 -.371* .344* 6 ------------------------------------------------------------7 ------------------------------------------------------------8 .042 .073 .237 .383* .210* 9 .357* .064 -.119 -.082 .153 10 -.101 -.316 .003 -.240 .168 11 -.234 -.002 -.191 -.055 .094 12 -.132 -.009 .044 -.731** .554** 13 .274 -.017 .105 .021 .087 14 -.048 -.078 .385* -.011 .156 15 -.400** -.043 -.156 -.713** .695** 16 -.072 -.217 -.362* -.217 .230 17 -.210 .242 -.131 -.439** .313* 18 -.205 .184 -.263 .130 .162 19 -.201 .285 -.169 -.487** .387** 20 -.158 -.338 .197 -.069 .183 21 .073 -.081 -.245 -.549** .373* 22 -.102 -.102 -.102 -.408* .198 23 -.294* -.173 .086 -.708** .626** 24 -.248 -.149 -.133 -.514** .366* 25 -.382* .123 -.140 -.370* .318* 26 -.181 .211 .215 .126 .139 27 -.443** -.268 .011 -.313* .366* 28 -.180 .180 .180 -.180 .129 29 -.300 -.050 -.383* -.383* .385** 30 -.107 .010 .193 -.193 .086 31 ------------------------------------------------------------32 -.144 .155 .122 -.580** .396** 33 .082 .009 -.111 -.043 .021 34 -.223 .287 -.053 -.223 .185 35 -.122 -.049 .146 .122 .054 36 -.323* -.218 -.152 -.491** .416** 37 ------------------------------------------------------------38 .065 -.013 .079 -.425* .192 39 .343* .023 .080 -.377* .267 40 -.051 -.024 .133 -.403* .183 41 .049 .215 -.004 -.787** .669** 42 -.185 -.042 .102 -.341 .163 43 .359* .072 .049 -.113 .149

PAGE 91

91 Table A-3. Continued Participant Age Race Sex Pain R 44 -.108 .149 .108 -.316 .146 45 -.181 .010 -.341* -.366* .283* 46 -.473** -.108 .028 -.512** .509** 47 -.350** .091 -.141 -.662** .590** 48 -.278 -.081 -.038 -.568** .408** 49 -.454** -.307* -.043 -.392** .456** 50 -.226 .111 -.116 -.368* .213 51 ------------------------------------------------------------52 -.134 .370* .195 .082 .200 53 -.240 -.110 -.240 -.386* .277 54 .010 -.169 -.129 -.618** .427** **p<.01,*p<.05, p<.10

PAGE 92

92 Table A-4. Policies toward negative mood assessment Participant Age Race Sex Pain R 1 .131 -.235 .328* .508** .437** 2 -.020 -.526** .245 .032 .338* 3 -.184 -.033 -.080 -.071 .046 4 .049 .272** -.065 .819** .751** 5 .521** .201 .100 .632** .722** 6 -.060 .018 -.025 .032 .006 7 .374** .045 -.296* .625** .621** 8 .195 -.039 .000 .311 .136 9 .343* .254 .299 .359* .401** 10 .002 .190 -.144 .478** .285* 11 -.022 -.009 .013 .737** .543** 12 -.133 .241* .148 .758** .673** 13 .136 .009 -.032 .054 .023 14 .181 -.277 .443** .082 .313* 15 .283** -.054 -.001 .802** .726** 16 .038 .038 .419* -.063 .182 17 .266 -.056 .134 .545** .389** 18 .077 -.166 .191 .444* .267 19 .118 -.042 .216 .477** .290* 20 .087 .380* .000 .075 .158 21 -.042 .139 .412** .550** .494** 22 .146 .073 .000 .656** .457** 23 .262* .102 .080 .737** .629** 24 -.120 -.075 -.014 .636** .425** 25 .180 -.075 .122 .600** .413** 26 .205 .255 .105 -.173 .148 27 .163 .186* -.267** .804** .779** 28 .226 .233 .089 .192 .151 29 .366* .095 .417 .219 .364* 30 .042 .228 .111 .377* .208 31 .121 -.198 .179 .051 .089 32 .165 .012 .053 .628** .424** 33 .117 -.011 -.117 .156 .052 34 -.188 -.156 -.007 .656** .491** 35 -.396* .044 .242 .000 .217 36 .308 -.007 .289 .365* .312* 37 .033 .156 .325* .644** .547** 38 .225 .121 -.012 .422* .244 39 -.120 .172 -.190 .310 .176 40 .130 .370** -.003 .625** .545** 41 -.029 .079 .096 .901** .828** 42 .100 -.204 .194 .541** .382** 43 -.013 .136 -.069 .019 .024

PAGE 93

93 Table A-4. Continued Participant Age Race Sex Pain R 44 .171 -.014 .282** .788** .729** 45 .123 -.043 .305 .376* .251 46 .408** -.052 -.051 .609** .543** 47 .336** -.034 .138 .709** .635** 48 .248 .047 .033 .603** .429** 49 .096 .183 .057 .535** .332* 50 .105 .156 -.228 .167 .115 51 ------------------------------------------------------------52 .169 .216 -.005 .084 .082 53 .147 .032 .015 .653** .450** 54 -.145 .101 .261 .630** .496** **p<.01,*p<.05, p<.10

PAGE 94

94 Table A-5. Policies toward non-opioid treatment Participant Age Race Sex Pain R 1 -.151 .076 -.138 .087 .056 2 .040 .546** .022 .262 .369* 3 -.180 -.180 .180 -.180 .129 4 .116 -.198 -.131 -.282 .150 5 .081 -.244 -.244 .081 .132 6 .180 -.180 .180 .180 .129 7 ------------------------------------------------------------8 -.037 .213 .120 .409* .228 9 .200 -.035 .118 .259 .123 10 .106 .152 -.014 -.350 .157 11 .438* -.045 -.135 .191 .248 12 -.029 -.001 .229 .550** .355* 13 .273 -.020 .164 .076 .108 14 .184 .114 .461** -.386* .409** 15 .288 -.079 .068 .519** .363* 16 .012 .229 .157 -.326 .184 17 -.171 -.284 -.124 -.316 .225 18 .136 .311 -.116 -.017 .129 19 ------------------------------------------------------------20 .020 .256 .256 .020 .132 21 .049 .117 .336* .512** .391** 22 ------------------------------------------------------------23 .010 -.070 -.142 .272 .099 24 -.039 -.015 -.042 .491** .245 25 .173 -.191 -.180 .176 .130 26 .181 .181 .181 -.178 .130 27 .088 -.037 .209 -.571** .379** 28 -.133 .035 .262 -.069 .092 29 .008 .259 .028 .540** .359* 30 .127 -.017 .081 .168 .051 31 -.180 -.180 -.180 .180 .129 32 .229 .229 .115 .229 .171 33 -.093 -.219 -.205 .000 .099 34 .200 -.399* -.105 .244 .270 35 -.180 .180 -.180 -.180 .129 36 -.511** -.057 -.112 -.468** .496** 37 -.244 -.081 -.081 -.081 .079 38 .000 .258 .000 -.258 .133 39 .239 -.278 -.017 -.231 .188 40 -.161 -.132 -.110 -.537** .343* 41 -.074 -.247 .247 .247 .188 42 -.027 .184 .048 .233 .091 43 -.201 -.201 .033 .201 .122

PAGE 95

95 Table A-5. Continued Participant Age Race Sex Pain R 44 -.128 .222 .222 -.128 .131 45 -.229 -.200 .135 -.232 .165 46 -.237 -.081 .133 -.352 .214 47 -.334* .116 -.040 -.618** .509** 48 -.348* -.100 -.016 -.557** .442** 49 -.391* -.014 -.251 -.334* .327* 50 .089 .164 -.202 -.089 .083 51 -.180 -.180 .180 .180 .129 52 .150 -.354* -.150 -.107 .182 53 .269 .103 .024 .515** .349* 54 ------------------------------------------------------------**p<.01,*p<.05, p<.10

PAGE 96

96 Table A-6. Policies toward opioid treatment Participant Age Race Sex Pain R 1 .052 .054 .365* .171 .168 2 .199 .383* .003 .199 .226 3 .180 .180 .180 -.180 .129 4 .067 .217* -.004 .875** .818** 5 .378* .076 .098 .489** .397** 6 ------------------------------------------------------------7 .201 -.402* -.126 -.101 .228 8 -.019 .099 .370* .540** .439** 9 .264 .274 .155 .174 .199 10 -.040 .131 -.180 .454** .257 11 .024 -.142 .086 .665** .470** 12 -.002 .084 .219 .740** .603** 13 .331 .085 .299 -.121 .221 14 .290 -.012 .343* .367* .337* 15 .117 -.016 .078 .776** .622** 16 .008 -.040 .233 -.040 .057 17 .252 .200 .111 .611** .489** 18 .040 -.181 .211 .346 .199 19 .199 -.094 .241 .455** .314* 20 .008 .341 -.083 .000 .123 21 -.278* .312* .141 .626** .587** 22 ------------------------------------------------------------23 .302** -.077 .116 .776** .713** 24 -.112 -.007 -.089 .464** .235 25 .084 .119 .417* .249 .257 26 .180 .180 .180 -.180 .129 27 .109 .134 -.181 .847** .779** 28 .271 .072 -.151 .268 .173 29 .349** .170 .329** .611** .632** 30 .089 .135 .061 -.020 .031 31 ------------------------------------------------------------32 .133 -.008 .003 .512** .280 33 -.095 .469** -.163 .060 .259 34 -.008 -.159 -.099 .750** .598** 35 .107 -.322 -.107 -.107 .138 36 .154 -.144 .248 .532** .389** 37 .177 -.183 -.183 .177 .129 38 .102 .397** .151 .497** .438** 39 .026 -.096 -.411* .236 .235 40 .124 .333** .131 .730** .676** 41 -.106 -.025 .037 .920** .860** 42 -.027 .168 .049 -.200 .071 43 -.251 .142 -.050 -.042 .088

PAGE 97

97 Table A-6. Continued Participant Age Race Sex Pain R 44 -.151 -.151 .001 .189 .081 45 .031 .305 .209 .279 .216 46 .135 .144 -.050 .360 .180 47 .341** -.086 .125 .717** .653** 48 .182 .158 .006 .594** .411** 49 .337* .049 .141 .447** .335* 50 .298 -.053 .165 -.074 .124 51 .180 .180 -.180 -.180 .129 52 .147 -.332 -.147 -.114 .166 53 .305* -.065 .033 .656** .528** 54 .002 .034 .297* .625** .480** **p<.01,*p<.05, p<.10

PAGE 98

98 Table A-7. Policies toward change in non-opioid treatment Participant Age Race Sex Pain R 1 .090 -.114 .072 .094 .035 2 .194 .230 .035 .000 .092 3 .132 .019 -.345 .194 .174 4 .298 -.007 -.047 -.040 .093 5 .320 .110 .129 .320 .234 6 .019 .119 .119 .268 .100 7 .173 -.307 .040 -.360* .256 8 -.097 .194 .252 .180 .143 9 -.464** -.282 -.001 -.145 .316* 10 .008 .008 .133 -.174 .048 11 -.078 .287 -.088 .511** .357* 12 .054 .081 .092 .774** .618** 13 .341 .080 .211 -.011 .167 14 .156 -.164 .499** -.110 .312* 15 .216 -.076 .234 .579** .443** 16 .030 .030 .355* -.315 .227 17 ------------------------------------------------------------18 ------------------------------------------------------------19 .211 -.063 .147 .523** .344* 20 .180 .180 .180 .180 .129 21 -.141 .218 .374** .609** .578** 22 .279 .279 -.200 .153 .219 23 .216 -.027 -.084 .463** .268 24 -.089 .092 -.064 .614** .398** 25 .087 -.167 -.107 .167 .075 26 .286 .283 .095 -.157 .196 27 .042 .293 -.293 .484** .407** 28 -.051 -.118 .185 -.138 .070 29 .291 -.243 .271 .018 .218 30 .040 .056 .032 -.040 .007 31 -.264 -.242 -.022 -.132 .146 32 .183 .183 -.176 .176 .129 33 -.086 -.121 -.159 .159 .072 34 -.111 -.144 .202 .573** .403** 35 ------------------------------------------------------------36 -.409* -.203 .041 -.098 .220 37 .180 .356** .150 .651** .604** 38 -.012 -.292 .226 .074 .142 39 .178 -.148 -.294 -.011 .140 40 -.180 -.180 -.180 .180 .129 41 -.184 -.300* .074 .657** .560** 42 .336* .237 .013 .324 .274 43 -.248 .235 .172 -.146 .168

PAGE 99

99 Table A-7. Continued Participant Age Race Sex Pain R 44 .177 -.149 -.012 .152 .077 45 -.167 -.237 .073 -.261 .157 46 -.109 .110 .031 -.270 .096 47 -.209 .195 -.086 -.540** .381** 48 -.370* -.108 .042 -.500** .400** 49 -.280 -.280 -.038 -.082 .165 50 -.152 .281 .094 -.304 .203 51 ------------------------------------------------------------52 .190 .224 -.184 .034 .121 53 .115 -.187 .209 .587** .436** 54 .257 -.244 .244 .068 .190 **p<.01,*p<.05, p<.10

PAGE 100

100 Table A-8. Policies toward change in opioid treatment Participant Age Race Sex Pain R 1 .127 .030 .372* .260 .223 2 .204 .240 .027 .027 .101 3 .222 .146 -.089 .349* .200 4 .057 .065 .057 .789** .633** 5 ------------------------------------------------------------6 .051 .107 .006 .264 .084 7 .064 -.295 .013 -.218 .138 8 .083 .124 .185 .447* .257 9 -.303 -.321 -.096 -.096 .213 10 .004 .162 -.131 .403* .206 11 -.051 .182 .005 .466** .253 12 -.043 .112 .288* .703** .592** 13 .218 .161 .100 .157 .108 14 .192 .304 .304 .155 .245 15 .049 -.049 -.008 .824** .683** 16 -.063 .032 .317 -.016 .106 17 .180 -.180 .180 .180 .129 18 ------------------------------------------------------------19 .211 -.039 .135 .507** .321* 20 -.034 .242 -.126 -.040 .077 21 -.346* .277 .159 .255 .287* 22 -.027 .311 -.030 .252 .162 23 .281** -.091 .091 .797** .731** 24 -.038 -.134 -.128 .520** .306* 25 -.068 .166 .296 .178 .151 26 .016 .016 .016 -.016 .001 27 .000 .216 -.304* .716** .652** 28 .065 -.015 .225 -.210 .099 29 .286 -.123 .253 .335* .273 30 .109 .199 .032 .045 .055 31 -.206 -.186 .021 -.062 .081 32 .111 .095 .118 .195 .073 33 -.034 .393* -.132 .247 .234 34 -.035 -.090 .152 .676** .489** 35 .180 -.180 -.180 .180 .129 36 -.036 -.039 .175 .511** .294* 37 .246 .293* .170 .567** .497** 38 .077 .109 .258 .413* .255 39 .059 -.075 -.138 -.083 .035 40 -.122 .122 -.122 .320 .147 41 -.144 .047 -.005 .730** .556** 42 .346* .223 .002 .329* .278 43 .173 .065 .108 .087 .054

PAGE 101

101 Table A-8. Continued Participant Age Race Sex Pain R 44 .320 .000 .160 .320 .231 45 -.128 .151 .105 .206 .093 46 -.098 .306 -.219 .281 .241 47 .392** -.073 .083 .652** .592** 48 .154 .184 -.011 .563** .374* 49 .122 .267 -.071 .361* .222 50 .409* -.027 .264 -.002 .237 51 -.180 .180 .180 -.180 .129 52 .091 .310 -.125 .100 .130 53 .090 .167 .070 .583** .381** 54 -.261 .197 .146 .501** .380** **p<.01,*p<.05, p<.10

PAGE 102

102 LIST OF REFERENCES Acute Pain Management Guideline Panel. Acute pain management: Operative or medical procedures and trauma. Clinical practice Guideline. (1992). Rockville, MD: Agency for Health Care Policy and Research, Public Health Service, U.S. Department of Health and Human Services. Agency for Health Care Policy and Research. Management of Cancer Pain. (1994). Rockville, MD: Agency for Health Care Policy and Re search, Public Health Service, U.S. Department of Health and Human Services. Ahles, T. A., Coombs, D. W., Jensen, L., Stuke l, T., Maurer, L. H., & Keefe, F. J. (1990). Development of a behavioral observation tec hnique for the assessment of pain behaviors in cancer patients. Behavior Therapy, 21, 449-460. Alspach, J. G. (2000). From staff nurse to pr eceptor: A preceptor development program (2nd ed.). Aliso Viejo, CA: American Asso ciation of Critical Care Nurses. American Geriatrics Society Panel on Persistent Pain in Older Persons. (2002). The management of persistent pain in older persons. Journal of the American Geriatrics Society, 50, 205-224. American Medical Association. (2005). Physician characteristics and distribution in the U.S. Chicago, IL: American Medical Association. American Pain Society. (1992). Principles of analgesic use in the treatment of acute pain and chronic cancer pain (3rd ed.). Skokie, IL: American Pain Society. Anderson, G., & Horvath, J. (2002). Chronic conditions: Making the case for ongoing care Princeton, NJ: Robert Wood Johnson Foundations Partnership for Solutions. Anderson, K. O., Mendoza, T. R., Valero, V., Ri chman, S. P., Russell, C., Hurley, J., et al. (2000). Minority cancer patients and their providers: pain management attitudes and practice. Cancer, 88, 1929-1938. Aubrun, F. (2005). Management of postope rative analgesia in elderly patients. Regional Anesthesia and Pain Medicine, 30, 363-379. Auret, K., & Schug, S. A. (2005). Underutilisation of opioids in elderly patients with chronic pain: Approaches to correcting the problem. Drugs & Aging, 22, 641-654. Bartfield, J. M., Salluzzo, R. F., Raccio-Roba k, N., Funk, D. L., & Verdile, V. P. (1997). Physician and patient factors influenc ing the treatment of low back pain, Pain, 73, 209-211. Beal, D., Gillis, J. S., & Stewart, T. (1978). The lens model: Computational procedures an applications. Perceptual & Motor Skills, 46, 3-28.

PAGE 103

103 Bellville, J. W., Forrest, W. H ., Miller, E., & Brown, B. W. (1971). Influence of age on pain relief from analgesics. JAMA, 217, 1835-1841. Bernabei, R., Gambassi, G., Lapane, K., Landi, F., Gatsonis, C., Dunlop, R., et al. (1998). Management of Pain in Elde rly Patients with Cancer. JAMA, 279, 1877-1882. Beyer, J. E., DeGood, D. E., Ashley, L. C., & Ru ssell, G. A. (1983). Patterns of postoperative analgesic use with adults and children following cardiac surgery. Pain, 17, 71-81. Bier, M. (1990). A comparison of the degree of ra cism, sexism, and homophobia between beginning and advanced psychology students. Unpublished masters thesis, East Carolina University, Greenville, NC. Bond, M. R. (1971). Pain in hospital. Lancet, 1, 37. Bond, M. R., & Pilowsky, I. (1966). Subjective a ssessment of pain and its relationship to the administration of analgesics in patients with advanced cancer. Journal Psychosomatic Research, 10, 203-208. Borod, J. C., Yecker, S. A., Brickman, A. M., Moreno, C. R., Sliwinski, M., Foldi, N. S., Alpert, M., Welkowitz, J. (2004). Changes in posed f acial expression of em otion across the adult life span. Experimental Ag ing Research, 30, 305-331. Breau, L. M., McGrath, P. J., Cr aig, K. D., Santor, D., Cassid y, K-L., & Reid, G. J. (2001). Facial expression of children receiving immunizations: A pr incipal components analysis of the child facial coding system. Clinical Journal of Pain, 17, 178-186. Breitbart, W., McDonald, M. V., Rosenfeld, B., Passik, S. D., Hewitt, D., Thaler, H., et al. (1996). Pain in ambulatory AIDS patients. I. Pain characteristics a nd medical correlates. Pain, 68, 315-321. Buchan, J. (2005) A certain ratio ? The policy implications of mi nimum staffing ratios in nursing. Journal of Health Services Research and Policy, 10, 239-247. Calderone, K. L. (1990). The influence of ge nder on the frequency of pain and sedative medication administration to postoperative patients. Sex Roles, 23, 713-725. Campbell, L. C. (2002). Predispositions towa rd pharmacological pain management: A policy capturing study (Doctoral Dissertation, University of Florida, 2002). Dissertation Abstracts International, 63, 4892. Campbell, C. M., Edwards, R. R., & Fillingim, R. B. (2005). Ethnic differences in responses to multiple experimental pain stimuli. Pain, 113, 20-26. Casanova, J., Day, K., Dorpat, D., Hendricks, B., Theis, L., & Wiesman, S. (2007). Nursephysician work relations and role expectations. Journal of Nursing Administration, 37, 68-70.

PAGE 104

104 Chakour, M. C., Gibson, S. J., Bradbeer, M., & Helme, R. D. (1996). The effect of age on Adelta and C fibre thermal pain perception. Pain, 64, 143-152. Choiniere, M. Melzack, R., Girard, N., Rondequ, J., & Paqui n, M. J. (1990). Comparisons between patients and nurses assessment of pa in and medication efficacy in severe burn injuries. Pain, 40, 143-52. Cleeland, C. S., Gonin, R., Baez, L., Loehrer, P. & Pandya, K. J. (1997). Pain and treatment of pain in minority patients with cancer. Annals of Internal Medicine, 127, 813-816. Cleeland, C. S., Gonin, R., Hatfield, A. K., Edmons on, J. H., Blum, R. H., Stewart, J. A., et al. (1994). Pain and its treatment in out patients with metastatic cancer. NEJM, 330, 592-596. Cohen, F. L. (1980). Postsurgical pain relief: Patients status and nur ses medication choices. Pain, 9, 265-74. Cohen-Mansfield, J., & Lipson, S. L. (2002a). Pain in cognitively impaired nursing home residents: How well are physicians diagnosing it? Journal of the American Geriatrics Society, 50, 1039-1044. Cohen-Mansfield, J., & Lipson, S. (2002b). The unde rdetection of pain of dental etiology in persons with dementia. American Journal of Alzheimer s Disease and Other Dementias, 17, 249-253. Cook, A. K., Niven, C., & Downs, M. G. (1999) Assessing the pain of people with cognitive impairment. International Journal of Geriatric Psychiatry, 14, 421-425. Cooksey, R. W. (1996). Judgment analysis: Theory, methods, and applications. San Diego, CA: Academic Press. Cousins, M. J. (1991). Prevention of postoperative pain. In M. R. Bond, J. E. Charlton, & C. J. Woolf (Eds.), Pain research and clinical management: Vol. 4. Proceedings of the 6th World Congress on Pain. (pp. 41-50). New York, NY: Elsevier Cousins, M. (1994). Acute and postoperative pain. In P. Wall, & R. Melzack (Eds.), Textbook of pain. (pp. 357-385). Edinburgh, Scotland: Churchill Livingstone. Craig, K. D. (1980). Ontogenetic and cultural influe nces on the expression of pain in man. In H. W. Kosterlitz, & L. Y. Terenius (Eds.), Pain and society. (pp. 39-52). Weinheim: Verlag Chemie. Craig, K. D. (1992). The facial expression of pain: better than a thousand words? APS Journal, 1, 153-162. Craig, K. D., Hyde, S. A., & Patrick, C. J. (1991) Genuine, suppressed and faked facial behavior during exacerbation of chronic low back pain. Pain, 46, 161-171.

PAGE 105

105 Craig, K. D., & Prkachin, K. M. (1983). Nonverb al measures of pain. In R. Melzack (Ed.), Pain measurement and assessment. (pp. 173-179). New York: Raven Press. Craig, K. D., Prkachin, K. M., & Grunau, R. (1992). The facial expression of pain. In D. Turk, & R. Melzack (Eds.), Handbook of pain assessment (pp. 257-276). New York: Guilford Press. Craig, K. D., Whitfield, M. F., Grunau, R. V. E., Linton, J., & Hadjistavropoulos, H. D. (1993). Pain in the preterm neonate: Be havioural and physiological indices. Pain, 52, 287-299. Creamer, P., Lethbridge-Cejku, M., & Hochberg, M. C. (1999). Determinants of pain severity in knee osteoarthritis: Effect of demographi c and psychosocial variables using 3 pain measures. Journal of Rheumatology, 26, 1785-1792. Darwin, C. (1872/1965). The expression of the emotions in man and animals. Chicago: University of Chicago Press. (Original work published 1872) van Dongen, K. A. J., Abu-Saad, H. H., & Hamers, J. P. H. (1999). On the development of an observational scale to measure pain in nonverbal children with severe or profound cognitive impairment; collecting the indicators. Proceedings of the 9th World Congress of Pain. (p. 87). Seattle: IASP Press. Edwards, R. R., Fillingim, R. B., & Ness, T. J. (2003). Age-related differences in endogenous pain modulation: A comparison of diffuse noxi ous inhibitory controls in healthy older and younger adults. Pain, 101, 155-165. Edwards, R. R., & Fillingim, R. B. (1999). Et hnic differences in thermal pain responses. Psychosomatic Medicine, 61, 346-354. Ekman, P. (1992). An argument for basic emotions. Cognition and Emotion, 6, 169-200. Ekman, P. (1994). Strong evidence for universals in facial expressions: A reply to Russells mistaken critique. Psychological Bulletin, 113, 268-287. Ekman, P., & Friesen, W. V. (1969 ). The repertoire of nonverbal behavior: Categories, origins, usage, and coding. Semiotica, 1, 49-98. Ekman, P., & Friesen, W. V. (1978). Manual for the facial action coding system. Palo Alto, CA: Consulting Psychology Press. Ekman, P., & Friesen, W. V. (1971). Consta nts across cultures in the face and emotion. Journal of Personality and Social Psychology, 17, 124-129. Ekman, P., Friesen, W. V., & Ellsworth, P. (198 3). What components of facial behavior are related to observers' judgments of emotion? In P. Ekman (Ed.), Emotion in the human face. (pp. 98-110). Cambridge, England: Cambridge University Press.

PAGE 106

106 Ekman, P., Friesen, W. V., OSullivan, M., Chan, A ., Diacoyanni-Tarlatzis, I., Heider, K., et al. (1987). Universals and cultural differences in the judgments of facial expressions of emotion. Journal of Personality and Social Psychology, 53, 712-717. Etcoff, N. L. & Magee, J. J. (1992). Cate gorical perception of facial expression. C ognition, 44, 227-240. Faherty, B. S., & Grier, M. R. (1984). Analgesic medication for elderly people post-surgery. Nursing Research, 33, 369-372. Faucett, J., Gordon, N., & Levine J. (1994). Differences in po stoperative pain severity among four ethnic groups. Journal of Pain Symptom Management, 9, 383-389. Feldt, K. S., Ryden, M. B., & Miles, S. (1998). Treatment of pain in cognitively impaired compared with cognitively intact older patients with hip-fracture. Journal of the American Geriatrics Society, 46, 1079-1085. Ferrell, B. A. (1995). Pain evaluation and management in the nursing home. Annals of Internal Medicine, 123, 681-687. Ferrell, B. A. (1996). Overview of aging and pain In B. R. Ferrell, & B. A. Ferrell (Eds.), Pain in the elderly. (pp. 1-10). Seattle, WA: IASP Press. Ferrell, B. A. (2003). Acute and chro nic pain. In C. K. Cassel, R. M. Leipzig, H. J. Cohen, E. B. Larson, & D. E. Meier (Eds.), Geriatric medicine: An evidence-based approach (pp. 323-342). New York, NY: Springer-Verlag. Ferrell, B. A., Ferrell, B. R., & Rivera, L. ( 1995). Pain in cognitively impaired nursing home patients. Journal of Pain and Symptom Management, 10, 591-599. Ferrell, B. R., McGuire, D. B., & Donavan, M. I. (1993). Knowledge and beliefs regarding pain in a sample of nursing faculty. Journal of Professional Nursing, 9, 79-88. Fitzpatrick, J. J. (2007). Cultural competence in nur sing education revisited. Nursing Education Perspectives, 28, 5. Foster, M. C., Pardiwala, A., & Calthorpe, D. (2000). Analgesia requirements following hip fracture in the cognitively impaired. Injury, 31, 435-436. Fridlund, A. J. (1994). Human facial expression: An evolutionary view. San Diego, CA: Academic Press. Fries, B. E., Simon, S. E., Morris, J. N., Flodstr om, C., & Bookstein, F. L. (2001). Pain in U.S. nursing homes: Validating a pain scale for the Minimum Data Set. The Gerontologist, 41, 173-179. Fuentes, E. F., Kohn, M. A., & Neighbor, M. L. (2002). Lack of association between patient ethnicity or race and facture analgesia. Academic Emergency Medicine, 9, 910-915.

PAGE 107

107 Gagliese, L. (2001). Assessment of pain in elderl y people. In D. C. Turk, & R. Melzack (Eds.), Handbook of pain assessment. (pp. 119-133). New York, NY: Guilford Press. Gagliese, L., Jackson, M., Ritvo, P., Wowk, A., & Katz, J. (2000). Age is not an impediment to effective use of patient controlled analgesia by surgical patients. Anesthesiology, 93, 601-610. Gagliese, L., & Katz, J. (2003). Age differences in postoperative pain are scale dependent: A comparison of measures of pain intensity and quality in younger and older surgical patients. Pain, 103, 11-20. Girard, N. J. (2003). Men and nursing. AORN Journal, 77, 728-730. Gloth, F. M. (2000). Geriatric pai n. Factors that limit pain relief and increase complications. Geriatrics, 55, 46-54. Goldberg, M. A., & Remy-St. Louis, G. R. ( 1998). Understanding and treating pain in ethnically diverse patients. Journal of Clinical Psychol ogy in Medical Settings, 5, 343-356. Goodenough, B., Addicoat, L., Champion, G. D., Mc Inerney, M., Young, B., Juniper, K., et al. (1997). Pain in 4to 6-year-old children re ceiving intramuscular immunization injections: A comparison of the Faces Pain Scale with other self-report and behavioral measures. Clinical Journal of Pain, 13, 60-73. Grunau, R. V. E., & Craig, K. D. (1987). Pain expression in neonates: Facial action and cry. Pain, 28, 395-410. Hadjistavropoulos, H. D., & Craig, K. D. (1994) Acute and chronic low back pain: cognitive, affective, and behavioral dimensions. Journal of Consulting and Clinical Psychology, 62, 341-349. Hadjistavropoulos, T., LaChapelle, D. L., Had jistavropoulos, H. D., Green, S., & Asmundson, G. J. G. (2002). Using facial expressions to asse ss musculoskeletal pain in older persons. European Journal of Pain, 6, 179-187. Haidt, J., & Keltner, D. (1999). Culture and f acial expression: Open-e nded methods find more expressions and a grad ient of recognition. Cognition and Emotion, 13, 225-266. Hale, C., & Hadjistavropoulos, T. (1 997). Emotional components of pain. Pain Research and Management, 2, 217. Hardy, R., & Smith, R. (2001). En hancing staff development w ith a structured preceptor program. Journal of Nursing Care Quality, 15, 9-17. Harkins, S. W. (1996). Geriatric pa in. Pain perceptions in the old. Clinics in Geriatric Medicine, 12, 435-459.

PAGE 108

108 Helme, R. D., & Gibson, S. J. (1997). Pain in th e elderly. In T. S. Jensen, J. A. Turner, & Z. Wiesenfeld-Hallin (Eds.), Proceedings of the 8th World Congress on Pain: Progress in pain research and management (pp. 919-944). Seattle, WA: IASP Press. Herr K. (2002). Chronic pain: Challenges and assessment strategies. Journal of Gerontological Nursing, 28, 20-27. Herr, K. A., & Mobily, P. R. (1991). Complexitie s of pain assessment in the elderly. Clinical considerations. Journal of Gerontological Nursing, 17, 12-9. Hoffman, C., Rice, D., & Sung, H. Y. (1996). Persons with chronic conditi ons: Their prevalence and costs. JAMA, 276, 1473-1479. Holm, K., Cohen, F., Dudas, S., Medema, P., & Allen, B. (1989). Eff ect of personal pain experience on pain assessment. Journal of Nursing Scholarship, 21, 72-75. Horgas, A. L., & Elliott, A. F. (2004). Pain assessment and management in persons with dementia. Nursing Clinics of North America, 39, 593-606. Horgas, A. L., & Tsai, P. F. (1998). Analgesic dr ug prescription and use in cognitively impaired nursing home residents. Nursing Research, 47, 235-242. Hughes, K. H., & Hood, L. J. (2007). Teaching methods and an outcome tool for measuring cultural sensitivity in undergraduate nursing students. Journal of Transcultural Nursing, 18, 57-62. International Association fo r the Study of Pain. (1993). Curriculum on pain for schools of nursing. Seattle, WA: International Asso ciation for the Study of Pain. International Association fo r the Study of Pain. (1997). Curriculum on pain for schools of nursing. Seattle, WA: International A ssociation for the Study of Pain. Irvine, D., Sidani, S., Porter, H., OBrien-Passa l, L., Simpson, B., McGillis Hall, L., Graydon, J., DiCenso, A., Redelmeir, D., & Nagel, L. ( 2000). Organizational fact ors influencing nurse practitioners' role implementa tion in acute care settings. Canadian Journal of Nursing Leadership, 13, 28-35. Johnson, M. K., & Marini, M. M. (1998). Bridging the racial divide in the United States: The effect of gender. Social Psychology Quarterly, 61, 247-258. Kaasalainen, S. J., Robinson, L. K., Hartley, T., Middleton, J., Knezacek, S., & Ife, C. (1998). The assessment of pain in the cognitively impaired elderly: A literature review. Perspectives, 22, 2-8. Karani, R., & Meier, D. E. (2004). Systemic pharmacologic postoperative pain management in the geriatric orthopaedic patient. Clinical Orthopaedics and Re lated Research, 425, 26-34.

PAGE 109

109 Karpman, R. R., Del Mar, N., & Bay, C. (1997). Analgesia for emergency centers' orthopaedic patients: Does an ethnic bias exist? Clinical Orthopaedics and Related Research, 334, 270-275. LaChapelle, D. L., Hadjistavropoulos, T., & Crai g, K. D. (1999). Pain measurement in persons with intellectual disabilities. Clinical Journal of Pain, 15, 13-23. Lavsky-Shulan, M., Wallace, R. B., Kohout, F. J., Lemke, J. H., Morris, M. C., & Smith, I. M. (1985). Prevalence and functional co rrelates of low back pain in the elderly: the Iowa 65+ Rural Health Study. Journal of the American Geriatrics Society, 33, 23-28. LeResche, L. (1982). Facial expression in pain: a study of candid photographs. Journal of Nonverbal Behavior, 7, 46-56. LeResche, L., & Dworkin, S. F. (1988). Facial ex pression of pain and emotions in chronic TMD patients. Pain, 35, 71-78. Lilley, C. M., Craig, K. D., & Grunau, R. V. E. ( 1996). Rating the intensity of facial actions in infants and toddlers: Impact on effect size. Abstracts of the 8th World Congress on Pain. Seattle, WA: IASP Press. Lindh, V., Wiklund, U., Sandman, P. O., & Hakanss on, S. (1997). Assessment of acute pain in preterm infants by evaluation of facial expre ssion and frequency domain analysis of heart rate variability. Early Human Development, 48, 131-142. Lipson, J. G., & DeSantis, L. A. (2007). Current a pproaches to integrati ng elements of cultural competence in nursing education. Journal of Transcultural Nursing, 18, 10S-20S. Malatesta, C., Izard, C. E., Culver, C., & Nico lich, M. (1987). Emotion communication skills in young, middle-aged and older women. Psychology and Aging, 2, 193-203. McCaffery, M., & Ferrell, B. R. (1992). Does the gender gap affect your pain management decisions? Nursing 92, 22, 48-51. McDonald, D. D. (1994). Gender and ethnic stereotyping and analgesic administration. Research in Nursing & Health, 17, 5-49. McDonald, D. D., & Bridge, R. G. (1991) Gender stereotyping and nursing care. Research in Nursing & Health, 14, 373-378. Melzack, R. (1975). The McGill Pain Questionna ire: major properties and scoring methods. Pain, 1, 277-299. Merskey, H., & Bogduk, N. (1994). Classification of chronic pain: Descriptions of chronic pain syndromes and definitions of pain terms (2nd ed.). (1994). Seattle, WA: International Association for the Study of Pain.

PAGE 110

110 Miller, G. A. (1956). The magical number seven, pl us or minus two: Some limits on our capacity for processing information. Psychological Review, 63, 81-97. Mobily, P. R., Herr, K. A., Clark, M. K., & Wall ace, R. B. (1994). An epidemiologic analysis of pain in the elderly: The Iowa 65+ Rural Health Study. Journal of Aging and Health, 6, 139-154. Montamat, S. C., Cusack, B. J., & Vestal, R. E. (1989). Management of drug therapy in the elderly. NEJM, 321, 303-309. Moore, A. R., & O'Keeffe, S. T. (1999). Druginduced cognitive impairment in the elderly. Drugs & Aging, 15, 15-28. Moreno, C., Borod, J., Welkowitz, J., & Alpert, M. (1993). The pe rception of facial emotion across the adult life span. Developmental Neuropsychology, 9, 305-314. Morgan, J., & Puder, K. (1989). Postoperative analgesia: Variations in prescribed and administered opioid dosages. In C. S. Hill, & W. S. Fields (Eds.), Advances in pain research and therapy. (pp. 175-180). New York, NY: Raven. Morrison, R. S., Magaziner, J., Gilbert, M. Kova l, K. J., McLaughlin, M. A., Orosz, G., et al. (2003). Relationship between pain and opioid analgesics on the development of delirium following hip fracture. Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 58, 76-81. Nelson, D. V., Novy, D. M., Averill, P. M., & Berry, L. A. (1996). Ethnic comparability of the MMPI in pain patients. Journal of Clinical Psychology, 52, 485-497. Ng, B., Dimsdale, J. E., Rollnik, J. D., & Shap iro, H. (1996a). The effect of ethnicity on prescriptions for patient controlle d analgesia for post-operative pain. Pain, 66, 9-12. Ng, B., Dimsdale, J. E., Shragg, G. P., & Deut sch, R. (1996b). Ethnic Differences in Analgesic Consumption for Postoperative Pain. Psychosomatic Medicine, 58, 125-29. Nishikawa, S. T., & Ferrell, B. A. (1993). Pain assessment in the elderly. Clinical Geriatrics and Issues in Long Term Care, 1, 15-28. Oberle, K., Paul, P., Wry, J., & Grace, M. (1990) Pain, anxiety and analgesics: A comparative study of elderly and younger surgical patients. Canadian Journal on Aging, 9, 13-22. Owen, H. Szekeley, J., Plummer, J., Cushnie, J ., & Mather, L. (1989). Va riations in patient controlled analgesia 2: Concurrent infusion. Anesthesia, 44, 11-13. Parmelee, P. A., Katz, I. R., & Lawton, M. P. (1991). The relation of pain to depression among institutionalized aged. Journal of Gerontology, 46, 15-21. Pilowsky, I., & Bond, M. R. (1969). Pain and its ma nagement in malignant disease. Elucidation of staff-patient transactions. Psychosomatic Medicine, 31, 400-404.

PAGE 111

111 Pollard, K. (2003). Searching for autonomy. Midwifery, 19, 113-124. Poole, G. D., & Craig K. D. (1992). Judgme nts of genuine, suppressed and faked facial expressions of pain. Journal of Personality and Social Psychology, 63, 797-805. Popp, B., & Portenoy, R. K. (1996). Management of chronic pain in the elderly: Pharmacology of opioids and other analgesic drugs. In B. R. Ferrell, & B. A. Ferrell (Eds.), Pain in the elderly. (pp. 21-34). Seattle, WA: IASP Press. Portenoy, R. K. (1996). Opioid therapy for chroni c nonmalignant pain: a re view of the critical issues. Journal of Pain and Symptom Management, 11, 203-217. Prkachin, K. M. (1992). The cons istency of facial expressions of pain: A comparison across modalities. Pain, 51, 297-306. Prkachin, K. M., Berzins, S. & Mercer, S. R. ( 1994) Encoding and decoding of pain expressions: A judgment study. Pain, 58, 253-259. Prkachin, K. M., & Mercer, S. R. (1989). Pain expression in patients w ith shoulder pathology: validity, properties and rela tionship to sickness impact. Pain, 39, 257-265. Prkachin, K. M., Solomon, P., Hwang, T., & Mer cer, S. R. (2001). Does experience influence judgments of pain behavior? Evidence from relatives of pain patients and therapists. Pain Research and Management, 6, 105-112. Qualls, R. C., Cox, M. B., & Schehr, T. L. ( 1992). Racial attitudes on campus: Are there gender differences? Journal of College Student Development, 33, 524-530. Robinson, J. H. (2000). Increasing students' cultura l sensitivity. A step toward greater diversity in nursing. Nurse Education, 25, 131-135. Robinson, M. E., & Wise, E. A. (2003). Gender bias in the observation of experimental pain. Pain, 104, 259-264. Robinson, M. E., Riley III, J. L., Myers, C. D., Pa pas, R. K., Wise, E. A., Waxenberg, L. B., et al. (2001). Gender role expecta tions of pain: Relationship to sex differences in pain. The Journal of Pain, 2, 251-257. Robinson, M. E., & Riley, J. L. (1998). Negative emotion in pain. In R. Gatchel, & D. Turk (Eds.), Psychosocial factors in pain. (pp. 74-88). New York: Guilford Press. Rooke, G. A., Reves, J. G., & Rosow, C. (2002). Anesthesiology and geriatric medicine (editorial). Anesthesiology, 96, 2-4. Ross, H. (2000). Lifting the unequal burden of cancer on minorities and the underserved. Closing the gap. Washington, DC: Office of Minority He alth, U.S. Department of Health and Human Services.

PAGE 112

112 Russell, J. A. (1994). Is there uni versal recognition of emotion fr om facial expression? A review of cross-cultural studies. Psychological Bulletin, 115, 102-141. Russell, J. A. (1995). Facial expressions of emotion: What lies beyond minimal universality? Psychological Bulletin, 118, 379-391. Salmon, P., & Manyande, A. (1996). Good patients c ope with their pain: postoperative analgesia and nurses' perceptions of their patients' pain. Pain, 68, 63-68. Sambamoorthi, U., Walkup, J., McSpiritt, E., Warner L., Castle, N., & Crystal, S. (2000). Racial differences in end-of-life care for patients with AIDS. AIDS Public Policy Journal, 15, 136-148. Sarkisian, C. A., Hays, R. D., Berry, S. H., & Mangione, C. M. (2001). E xpectations regarding aging among older adults and physicians who care for older adults. Medical Care, 39, 1025-1036. Schuman, H., Steeh, C., & Bobo, L. (1997). Racial attitudes in America: Trends and interpretations. Cambridge, MA: Harvard Univ. Press. Sengstaken, E. A., & King, S. A. (1993). The proble ms of pain and its detection among geriatric nursing home residents. Journal of the American Geriatrics Society, 41, 541-544. Shamian, J., & Inhaber, R. (1985). The concept and practice of preceptorship in contemporary nursing: A review of pertinent literature. International Journal of Nursing Studies, 22, 79-85. Sheiner, E. K., Sheiner, E., Shoham-Vardi, I., Mazor, M., & Katz, M. (1999). Ethnic differences influence care givers estim ates of pain during labour. Pain, 81, 299-305. Sherwood, M. B., Garcia-Siekavizza, A., Meltzer, M. I., Hebert, A., Burns, A. F., & McGorray, S. (1998). Glaucomas impact on quality of lif e and its relation to clinical indicators. Opthalmology, 105, 561-566. Speers, A., Strzyzewski, N., & Ziolkowski, L. ( 2004). Preceptor preparation: An investment in the future. Journal for Nurses in Staff Development, 20, 127-133 Stevens, B. J., Johnston, C. C., & Horton, L. (199 4). Factors that influence the behavioral pain responses of premature infants. Pain, 59, 101-109. Stewart, W. F., Lipton, R. B., & Liberman, J. ( 1996). Variation in migraine prevalence by race. Neurology, 47, 52-59. Tamayo-Sarver, J. H., Dawson, N. V., Hinze, S. W., Cydulka, R. K., Wigton, R. S., Albert, J. M., et al. (2003a). The effect of race/ethnic ity and desirable social characteristics on physicians' decisions to prescribe opioid analgesics. Academic Emergency Medicine, 10, 1239-1248.

PAGE 113

113 Tamayo-Sarver, J. H, Hinze, S. W., Cydulka, R. K., & Baker, D. W. ( 2003b). Racial and ethnic disparities in emergency department analgesic prescription. American Journal of Public Health, 93, 2067-2073. Teno, J. M., Weitzen, S., Wetle, T., & Mor, V. (2001) Persistent pain in nursing home residents. JAMA, 285, 2081. Teske, K., Daut, R. L., & Cleeland, C. S. (1983) Relationships between nurses' observations and patients' self-reports of pain. Pain, 16, 289-296. Thomas, T., Robinson, C., Champion, D., McKe ll, M., & Pell, M. (1998). Prediction and assessment of the severity of post-operative pain and of satisfaction with management. Pain, 75, 177-185. Todd, K. H., Deaton, C., DAdamo, A. P., & Goe, L. (2000). Ethnicity an d analgesic practice. Annals of Emergency Medicine, 35, 11. Todd, K. H., Lee, T., & Hoffman, J. R. (1994). Th e effect of ethnicity on physician estimates of pain severity in patients with isolated extremity trauma. JAMA, 271, 925-928. Todd, K. H., Samaroo, N., & Hoffman, J. R. (1993). Ethnicity as a risk fa ctor for inadequate emergency department analgesia. JAMA, 269, 1537-1539. Turk, D. C., & Okifuji, A. (1997). What factors affect physicians' decisions to prescribe opioids for chronic noncancer pain patients? Clinical Journal of Pain, 13, 330-336. Turk, D. C., & Okifuji, A. (1999). Does sex make a difference in the pr escription of treatments and the adaptation to chronic pain by cancer and non-cancer patients? Pain, 82, 139-148. Underwood, S. M. (2006). Culture, diversity, and h ealth: responding to the queries of inquisitive minds. Journal of Nursing Education, 45, 281-286. Walsh, N., Schoenfeld, L., Ramamurth, S., & Hoffman, J. (1989). Normative model for the cold pressor test. American Journal of Physical Medicine and Rehabilitation, 68, 6-11. Watt-Watson, J. H., Evernden, C., & Lawson, C. ( 1990). Parents percepti ons of their childs acute pain experience. Journal of Pediatric Nursing, 5, 344-349. Weiner, D., Peterson, B., & Keefe, F. (1999). Ch ronic pain-associated be haviors in the nursing home: Resident versus caregiver perceptions. Pain, 80, 577-588. Werner, P., Cohen-Mansfield, J., Wa tson, V., & Pasis, S. (1998). Pa in in participants of adult day care centers: Assessme nt by different raters. Journal of Pain and Symptom Management, 15, 8-17. White, K. E., & Cummings, J. E. (1997). Neurop sychiatric aspects of Alzheimer's disease and other dementing illnesses. In S. C. Yudofsky, & R. E. Hale (Eds.), The American

PAGE 114

114 Psychiatric Press textbook of neuropsychiatry. (pp. 823-854). Washington, DC: American Psychiatric Press. Williamson, G. M., & Schulz, R. (1992). Pain, ac tivity restriction, and symptoms of depression among community-residing elderly adults. Journal of Gerontology, 47, 367-372. Wise, E. A., Price, D. D., Myers, C. D., Heft M. W., & Robinson, M. E. (2002). Gender role expectations of pain: Relationship to experimental pain perception. Pain, 96, 335-342. Wolff, J. L., Starfield, B., & Anderson, G. ( 2002). Prevalence, expenditures, and complications of multiple chronic conditions in elderly. Archives of Inter nal Medicine, 162, 2269-2276. World Health Organization. (1986). Cancer Pain. Geneva: World Health Organization. Wuensch, K. L., Campbell, M. W., Kesler, F. C., & Moore, C. H. (2002). Racial bias in decisions made by mock jurors eval uating a case of sexual harassment. The Journal of Social Psychology, 142, 587-600. Young, A. W., Rowland, D., Calder, A. J., Etcoff, N. L., Seth, A., & Perrett, D. I. (1997). Facial expression megamix: Tests of dimensional and category accounts of emotion recognition. Cognition, 63, 271-313. Zalon, M. L. (1993). Nurses assessmen t of postoperative patients pain. Pain, 54, 329-334.

PAGE 115

115 BIOGRAPHICAL SKETCH Adam T. Hirsh received his B.A. in psychol ogy from the University of Central Florida in 2001. He subsequently enrolled in the doctoral progr am in Clinical and Health Psychology at the University of Florida. He was granted an M. S. in 2004, and following completion of a clinical internship at the VA Puget Sound Health Care Syst em, Seattle, he will gra duate with a Ph.D. in 2008. His clinical specialty is in behavioral medici ne, and his research inte rests are in the area of pain.