Relationship between Client Factors and Symptom Levels for Clients in Ongoing Mental Health Treatment

Permanent Link: http://ufdc.ufl.edu/UFE0011283/00001

Material Information

Title: Relationship between Client Factors and Symptom Levels for Clients in Ongoing Mental Health Treatment
Physical Description: Mixed Material
Copyright Date: 2008

Record Information

Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
System ID: UFE0011283:00001

Permanent Link: http://ufdc.ufl.edu/UFE0011283/00001

Material Information

Title: Relationship between Client Factors and Symptom Levels for Clients in Ongoing Mental Health Treatment
Physical Description: Mixed Material
Copyright Date: 2008

Record Information

Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
System ID: UFE0011283:00001

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 E20101129_AAAABI INGEST_TIME 2010-11-30T02:43:11Z PACKAGE UFE0011283_00001
113980 F20101129_AAAWNP leibert_t_Page_030.jp2
6399 F20101129_AAAWOE leibert_t_Page_079thm.jpg
27347 F20101129_AAAWNQ leibert_t_Page_116.QC.jpg
72453 F20101129_AAAWOF leibert_t_Page_034.jpg
73193 F20101129_AAAWNR leibert_t_Page_049.jpg
7011 F20101129_AAAWOG leibert_t_Page_060thm.jpg
133290 F20101129_AAAWNS leibert_t_Page_124.jp2
73602 F20101129_AAAWOH leibert_t_Page_022.jpg
7866 F20101129_AAAWNT leibert_t_Page_106.QC.jpg
24314 F20101129_AAAWOI leibert_t_Page_088.QC.jpg
113745 F20101129_AAAWNU leibert_t_Page_028.jp2
99657 F20101129_AAAWOJ leibert_t_Page_071.jp2
127361 F20101129_AAAWNV leibert_t_Page_121.jp2
1053954 F20101129_AAAWOK leibert_t_Page_074.tif
76004 F20101129_AAAWNW leibert_t_Page_033.jpg
F20101129_AAAWPA leibert_t_Page_120.tif
20142 F20101129_AAAWOL leibert_t_Page_129.QC.jpg
5979 F20101129_AAAWNX leibert_t_Page_027thm.jpg
6439 F20101129_AAAWPB leibert_t_Page_040thm.jpg
22911 F20101129_AAAWOM leibert_t_Page_079.QC.jpg
111639 F20101129_AAAWNY leibert_t_Page_064.jp2
22506 F20101129_AAAWPC leibert_t_Page_073.QC.jpg
6706 F20101129_AAAWON leibert_t_Page_065thm.jpg
6071 F20101129_AAAWNZ leibert_t_Page_011thm.jpg
2281 F20101129_AAAWPD leibert_t_Page_007thm.jpg
87694 F20101129_AAAWOO leibert_t_Page_115.jpg
23185 F20101129_AAAWOP leibert_t_Page_016.QC.jpg
6367 F20101129_AAAWPE leibert_t_Page_012thm.jpg
23319 F20101129_AAAWOQ leibert_t_Page_046.QC.jpg
F20101129_AAAWPF leibert_t_Page_034.tif
104579 F20101129_AAAWOR leibert_t_Page_055.jp2
113193 F20101129_AAAWPG leibert_t_Page_088.jp2
22269 F20101129_AAAWOS leibert_t_Page_130.jp2
7516 F20101129_AAAWPH leibert_t_Page_105.QC.jpg
23907 F20101129_AAAWOT leibert_t_Page_020.QC.jpg
F20101129_AAAWPI leibert_t_Page_113.tif
F20101129_AAAWOU leibert_t_Page_030.tif
14632 F20101129_AAAWPJ leibert_t_Page_070.QC.jpg
110574 F20101129_AAAWOV leibert_t_Page_046.jp2
24259 F20101129_AAAWPK leibert_t_Page_085.QC.jpg
73153 F20101129_AAAWOW leibert_t_Page_062.jpg
7042 F20101129_AAAWPL leibert_t_Page_117thm.jpg
5954 F20101129_AAAWOX leibert_t_Page_108thm.jpg
21806 F20101129_AAAWQA leibert_t_Page_061.QC.jpg
22463 F20101129_AAAWPM leibert_t_Page_074.QC.jpg
6460 F20101129_AAAWOY leibert_t_Page_044thm.jpg
110413 F20101129_AAAWQB leibert_t_Page_044.jp2
72867 F20101129_AAAWPN leibert_t_Page_036.jpg
73394 F20101129_AAAWOZ leibert_t_Page_092.jpg
92522 F20101129_AAAWQC leibert_t_Page_003.jp2
F20101129_AAAWPO leibert_t_Page_013.tif
F20101129_AAAWQD leibert_t_Page_048.tif
111722 F20101129_AAAWPP leibert_t_Page_049.jp2
62070 F20101129_AAAWQE leibert_t_Page_083.jpg
23954 F20101129_AAAWPQ leibert_t_Page_054.QC.jpg
111826 F20101129_AAAWPR leibert_t_Page_038.jp2
61872 F20101129_AAAWQF leibert_t_Page_129.jpg
18100 F20101129_AAAWPS leibert_t_Page_009.QC.jpg
69915 F20101129_AAAWQG leibert_t_Page_079.jpg
F20101129_AAAWPT leibert_t_Page_129.tif
13218 F20101129_AAAWQH leibert_t_Page_103.QC.jpg
45359 F20101129_AAAWPU leibert_t_Page_070.jpg
111585 F20101129_AAAWQI leibert_t_Page_022.jp2
6536 F20101129_AAAWPV leibert_t_Page_034thm.jpg
18840 F20101129_AAAWQJ leibert_t_Page_056.QC.jpg
F20101129_AAAWPW leibert_t_Page_122.tif
24548 F20101129_AAAWQK leibert_t_Page_033.QC.jpg
23545 F20101129_AAAWPX leibert_t_Page_019.QC.jpg
96974 F20101129_AAAWRA UFE0011283_00001.mets FULL
146498 F20101129_AAAWQL leibert_t_Page_114.jp2
F20101129_AAAWPY leibert_t_Page_126.tif
84050 F20101129_AAAWQM leibert_t_Page_009.jp2
F20101129_AAAWPZ leibert_t_Page_067.tif
23337 F20101129_AAAWQN leibert_t_Page_018.QC.jpg
24719 F20101129_AAAWRD leibert_t_Page_001.jpg
25952 F20101129_AAAWQO leibert_t_Page_120.QC.jpg
10297 F20101129_AAAWRE leibert_t_Page_002.jpg
105445 F20101129_AAAWQP leibert_t_Page_050.jp2
62398 F20101129_AAAWRF leibert_t_Page_003.jpg
F20101129_AAAWQQ leibert_t_Page_099.tif
109321 F20101129_AAAWQR leibert_t_Page_018.jp2
65562 F20101129_AAAWRG leibert_t_Page_005.jpg
69197 F20101129_AAAWQS leibert_t_Page_050.jpg
89575 F20101129_AAAWRH leibert_t_Page_006.jpg
25537 F20101129_AAAWQT leibert_t_Page_121.QC.jpg
23396 F20101129_AAAWRI leibert_t_Page_007.jpg
6559 F20101129_AAAWQU leibert_t_Page_086thm.jpg
33040 F20101129_AAAWRJ leibert_t_Page_008.jpg
6551 F20101129_AAAWQV leibert_t_Page_019thm.jpg
57781 F20101129_AAAWRK leibert_t_Page_009.jpg
F20101129_AAAWQW leibert_t_Page_111.tif
53656 F20101129_AAAWRL leibert_t_Page_010.jpg
6523 F20101129_AAAWQX leibert_t_Page_121thm.jpg
74073 F20101129_AAAWSA leibert_t_Page_029.jpg
71724 F20101129_AAAWRM leibert_t_Page_012.jpg
74500 F20101129_AAAWQY leibert_t_Page_090.jpg
74775 F20101129_AAAWSB leibert_t_Page_030.jpg
74488 F20101129_AAAWRN leibert_t_Page_013.jpg
6389 F20101129_AAAWQZ leibert_t_Page_130.QC.jpg
71771 F20101129_AAAWSC leibert_t_Page_032.jpg
71729 F20101129_AAAWRO leibert_t_Page_014.jpg
67887 F20101129_AAAWSD leibert_t_Page_035.jpg
70622 F20101129_AAAWRP leibert_t_Page_015.jpg
72560 F20101129_AAAWSE leibert_t_Page_037.jpg
71657 F20101129_AAAWRQ leibert_t_Page_016.jpg
72177 F20101129_AAAWSF leibert_t_Page_038.jpg
72764 F20101129_AAAWRR leibert_t_Page_019.jpg
70209 F20101129_AAAWSG leibert_t_Page_040.jpg
73092 F20101129_AAAWRS leibert_t_Page_020.jpg
74320 F20101129_AAAWRT leibert_t_Page_021.jpg
71786 F20101129_AAAWSH leibert_t_Page_041.jpg
66350 F20101129_AAAWRU leibert_t_Page_023.jpg
70328 F20101129_AAAWSI leibert_t_Page_042.jpg
58323 F20101129_AAAWRV leibert_t_Page_024.jpg
72308 F20101129_AAAWSJ leibert_t_Page_044.jpg
68267 F20101129_AAAWRW leibert_t_Page_025.jpg
71234 F20101129_AAAWSK leibert_t_Page_045.jpg
15377 F20101129_AAAWRX leibert_t_Page_026.jpg
72833 F20101129_AAAWTA leibert_t_Page_064.jpg
71541 F20101129_AAAWSL leibert_t_Page_046.jpg
65380 F20101129_AAAWRY leibert_t_Page_027.jpg
72543 F20101129_AAAWTB leibert_t_Page_065.jpg
73956 F20101129_AAAWSM leibert_t_Page_047.jpg
74475 F20101129_AAAWRZ leibert_t_Page_028.jpg
71479 F20101129_AAAWTC leibert_t_Page_066.jpg
72999 F20101129_AAAWSN leibert_t_Page_048.jpg
66643 F20101129_AAAWTD leibert_t_Page_067.jpg
75310 F20101129_AAAWSO leibert_t_Page_051.jpg
53869 F20101129_AAAWTE leibert_t_Page_068.jpg
70410 F20101129_AAAWSP leibert_t_Page_052.jpg
59296 F20101129_AAAWTF leibert_t_Page_069.jpg
71177 F20101129_AAAWSQ leibert_t_Page_053.jpg
67842 F20101129_AAAWTG leibert_t_Page_071.jpg
74542 F20101129_AAAWSR leibert_t_Page_054.jpg
57828 F20101129_AAAWTH leibert_t_Page_072.jpg
68494 F20101129_AAAWSS leibert_t_Page_055.jpg
58626 F20101129_AAAWST leibert_t_Page_056.jpg
70208 F20101129_AAAWTI leibert_t_Page_073.jpg
69108 F20101129_AAAWSU leibert_t_Page_057.jpg
69390 F20101129_AAAWTJ leibert_t_Page_074.jpg
67454 F20101129_AAAWSV leibert_t_Page_058.jpg
73874 F20101129_AAAWTK leibert_t_Page_075.jpg
79457 F20101129_AAAWSW leibert_t_Page_059.jpg
72736 F20101129_AAAWUA leibert_t_Page_098.jpg
60492 F20101129_AAAWTL leibert_t_Page_076.jpg
79168 F20101129_AAAWSX leibert_t_Page_060.jpg
72816 F20101129_AAAWUB leibert_t_Page_099.jpg
69349 F20101129_AAAWTM leibert_t_Page_077.jpg
67182 F20101129_AAAWSY leibert_t_Page_061.jpg
26440 F20101129_AAAWUC leibert_t_Page_100.jpg
61220 F20101129_AAAWTN leibert_t_Page_080.jpg
71900 F20101129_AAAWSZ leibert_t_Page_063.jpg
61358 F20101129_AAAWUD leibert_t_Page_101.jpg
65131 F20101129_AAAWTO leibert_t_Page_081.jpg
43737 F20101129_AAAWUE leibert_t_Page_103.jpg
13571 F20101129_AAAWTP leibert_t_Page_082.jpg
39257 F20101129_AAAWUF leibert_t_Page_104.jpg
73892 F20101129_AAAWTQ leibert_t_Page_084.jpg
22347 F20101129_AAAWUG leibert_t_Page_105.jpg
73746 F20101129_AAAWTR leibert_t_Page_085.jpg
F20101129_AAAXAA leibert_t_Page_031.tif
25062 F20101129_AAAWUH leibert_t_Page_106.jpg
72460 F20101129_AAAWTS leibert_t_Page_086.jpg
F20101129_AAAXAB leibert_t_Page_032.tif
20949 F20101129_AAAWUI leibert_t_Page_107.jpg
74845 F20101129_AAAWTT leibert_t_Page_088.jpg
F20101129_AAAXAC leibert_t_Page_033.tif
69993 F20101129_AAAWTU leibert_t_Page_089.jpg
F20101129_AAAXAD leibert_t_Page_035.tif
69575 F20101129_AAAWUJ leibert_t_Page_108.jpg
72831 F20101129_AAAWTV leibert_t_Page_091.jpg
F20101129_AAAXAE leibert_t_Page_037.tif
53146 F20101129_AAAWUK leibert_t_Page_109.jpg
71987 F20101129_AAAWTW leibert_t_Page_093.jpg
F20101129_AAAXAF leibert_t_Page_038.tif
36634 F20101129_AAAWUL leibert_t_Page_110.jpg
74155 F20101129_AAAWTX leibert_t_Page_094.jpg
F20101129_AAAXAG leibert_t_Page_040.tif
17160 F20101129_AAAWVA leibert_t_Page_128.jpg
27851 F20101129_AAAWUM leibert_t_Page_111.jpg
74538 F20101129_AAAWTY leibert_t_Page_095.jpg
F20101129_AAAXAH leibert_t_Page_041.tif
19585 F20101129_AAAWVB leibert_t_Page_130.jpg
79420 F20101129_AAAWUN leibert_t_Page_112.jpg
69888 F20101129_AAAWTZ leibert_t_Page_096.jpg
F20101129_AAAXAI leibert_t_Page_042.tif
25294 F20101129_AAAWVC leibert_t_Page_001.jp2
94140 F20101129_AAAWUO leibert_t_Page_114.jpg
F20101129_AAAXAJ leibert_t_Page_043.tif
5550 F20101129_AAAWVD leibert_t_Page_002.jp2
101302 F20101129_AAAWUP leibert_t_Page_116.jpg
F20101129_AAAXAK leibert_t_Page_044.tif
33725 F20101129_AAAWVE leibert_t_Page_004.jp2
87790 F20101129_AAAWUQ leibert_t_Page_117.jpg
F20101129_AAAXAL leibert_t_Page_045.tif
1051976 F20101129_AAAWVF leibert_t_Page_005.jp2
87931 F20101129_AAAWUR leibert_t_Page_118.jpg
F20101129_AAAXBA leibert_t_Page_061.tif
F20101129_AAAXAM leibert_t_Page_046.tif
1051956 F20101129_AAAWVG leibert_t_Page_006.jp2
81670 F20101129_AAAWUS leibert_t_Page_119.jpg
F20101129_AAAXBB leibert_t_Page_062.tif
F20101129_AAAXAN leibert_t_Page_047.tif
423096 F20101129_AAAWVH leibert_t_Page_007.jp2
90838 F20101129_AAAWUT leibert_t_Page_120.jpg
F20101129_AAAXBC leibert_t_Page_063.tif
F20101129_AAAXAO leibert_t_Page_049.tif
750901 F20101129_AAAWVI leibert_t_Page_008.jp2
89554 F20101129_AAAWUU leibert_t_Page_121.jpg
F20101129_AAAXBD leibert_t_Page_064.tif
F20101129_AAAXAP leibert_t_Page_050.tif
80841 F20101129_AAAWVJ leibert_t_Page_010.jp2
89867 F20101129_AAAWUV leibert_t_Page_123.jpg
F20101129_AAAXBE leibert_t_Page_065.tif
F20101129_AAAXAQ leibert_t_Page_051.tif
90886 F20101129_AAAWUW leibert_t_Page_124.jpg
F20101129_AAAXBF leibert_t_Page_066.tif
F20101129_AAAXAR leibert_t_Page_052.tif
101532 F20101129_AAAWVK leibert_t_Page_011.jp2
95116 F20101129_AAAWUX leibert_t_Page_125.jpg
F20101129_AAAXBG leibert_t_Page_068.tif
109092 F20101129_AAAWWA leibert_t_Page_032.jp2
F20101129_AAAXAS leibert_t_Page_053.tif
110165 F20101129_AAAWVL leibert_t_Page_012.jp2
F20101129_AAAXBH leibert_t_Page_070.tif
116310 F20101129_AAAWWB leibert_t_Page_033.jp2
F20101129_AAAXAT leibert_t_Page_054.tif
113516 F20101129_AAAWVM leibert_t_Page_013.jp2
88929 F20101129_AAAWUY leibert_t_Page_126.jpg
F20101129_AAAXBI leibert_t_Page_071.tif
111193 F20101129_AAAWWC leibert_t_Page_034.jp2
109709 F20101129_AAAWVN leibert_t_Page_014.jp2
103256 F20101129_AAAWUZ leibert_t_Page_127.jpg
F20101129_AAAXBJ leibert_t_Page_072.tif
112817 F20101129_AAAWWD leibert_t_Page_036.jp2
F20101129_AAAXAU leibert_t_Page_055.tif
107707 F20101129_AAAWVO leibert_t_Page_015.jp2
F20101129_AAAXBK leibert_t_Page_073.tif
112881 F20101129_AAAWWE leibert_t_Page_037.jp2
F20101129_AAAXAV leibert_t_Page_056.tif
110512 F20101129_AAAWVP leibert_t_Page_016.jp2
F20101129_AAAXBL leibert_t_Page_075.tif
108609 F20101129_AAAWWF leibert_t_Page_039.jp2
F20101129_AAAXAW leibert_t_Page_057.tif
109481 F20101129_AAAWVQ leibert_t_Page_017.jp2
F20101129_AAAXBM leibert_t_Page_077.tif
108162 F20101129_AAAWWG leibert_t_Page_040.jp2
F20101129_AAAXAX leibert_t_Page_058.tif
110857 F20101129_AAAWVR leibert_t_Page_019.jp2
F20101129_AAAXCA leibert_t_Page_092.tif
F20101129_AAAXBN leibert_t_Page_078.tif
109537 F20101129_AAAWWH leibert_t_Page_041.jp2
25271604 F20101129_AAAXAY leibert_t_Page_059.tif
112174 F20101129_AAAWVS leibert_t_Page_020.jp2
F20101129_AAAXCB leibert_t_Page_093.tif
F20101129_AAAXBO leibert_t_Page_079.tif
107901 F20101129_AAAWWI leibert_t_Page_042.jp2
F20101129_AAAXAZ leibert_t_Page_060.tif
114193 F20101129_AAAWVT leibert_t_Page_021.jp2
F20101129_AAAXCC leibert_t_Page_094.tif
F20101129_AAAXBP leibert_t_Page_080.tif
109245 F20101129_AAAWWJ leibert_t_Page_043.jp2
99031 F20101129_AAAWVU leibert_t_Page_023.jp2
F20101129_AAAXCD leibert_t_Page_095.tif
F20101129_AAAXBQ leibert_t_Page_081.tif
109304 F20101129_AAAWWK leibert_t_Page_045.jp2
88761 F20101129_AAAWVV leibert_t_Page_024.jp2
F20101129_AAAXCE leibert_t_Page_096.tif
F20101129_AAAXBR leibert_t_Page_082.tif
104004 F20101129_AAAWVW leibert_t_Page_025.jp2
F20101129_AAAXCF leibert_t_Page_097.tif
F20101129_AAAXBS leibert_t_Page_084.tif
112376 F20101129_AAAWWL leibert_t_Page_047.jp2
15431 F20101129_AAAWVX leibert_t_Page_026.jp2
F20101129_AAAXCG leibert_t_Page_098.tif
101306 F20101129_AAAWXA leibert_t_Page_067.jp2
F20101129_AAAXBT leibert_t_Page_085.tif
111629 F20101129_AAAWWM leibert_t_Page_048.jp2
99071 F20101129_AAAWVY leibert_t_Page_027.jp2
F20101129_AAAXCH leibert_t_Page_100.tif
80069 F20101129_AAAWXB leibert_t_Page_068.jp2
F20101129_AAAXBU leibert_t_Page_086.tif
116172 F20101129_AAAWWN leibert_t_Page_051.jp2
113588 F20101129_AAAWVZ leibert_t_Page_029.jp2
F20101129_AAAXCI leibert_t_Page_101.tif
89774 F20101129_AAAWXC leibert_t_Page_069.jp2
106135 F20101129_AAAWWO leibert_t_Page_052.jp2
F20101129_AAAXCJ leibert_t_Page_102.tif
65055 F20101129_AAAWXD leibert_t_Page_070.jp2
115316 F20101129_AAAWWP leibert_t_Page_054.jp2
F20101129_AAAXCK leibert_t_Page_103.tif
84239 F20101129_AAAWXE leibert_t_Page_072.jp2
F20101129_AAAXBV leibert_t_Page_087.tif
87759 F20101129_AAAWWQ leibert_t_Page_056.jp2
F20101129_AAAXCL leibert_t_Page_104.tif
105239 F20101129_AAAWXF leibert_t_Page_073.jp2
F20101129_AAAXBW leibert_t_Page_088.tif
101862 F20101129_AAAWWR leibert_t_Page_057.jp2
F20101129_AAAXDA leibert_t_Page_127.tif
F20101129_AAAXCM leibert_t_Page_105.tif
103488 F20101129_AAAWXG leibert_t_Page_074.jp2
F20101129_AAAXBX leibert_t_Page_089.tif
104636 F20101129_AAAWWS leibert_t_Page_058.jp2
F20101129_AAAXDB leibert_t_Page_128.tif
F20101129_AAAXCN leibert_t_Page_106.tif
111251 F20101129_AAAWXH leibert_t_Page_075.jp2
F20101129_AAAXBY leibert_t_Page_090.tif
1051903 F20101129_AAAWWT leibert_t_Page_059.jp2
F20101129_AAAXDC leibert_t_Page_130.tif
F20101129_AAAXCO leibert_t_Page_107.tif
87407 F20101129_AAAWXI leibert_t_Page_076.jp2
F20101129_AAAXBZ leibert_t_Page_091.tif
1051985 F20101129_AAAWWU leibert_t_Page_060.jp2
2492 F20101129_AAAXDD leibert_t_Page_001thm.jpg
F20101129_AAAXCP leibert_t_Page_108.tif
102297 F20101129_AAAWXJ leibert_t_Page_077.jp2
102306 F20101129_AAAWWV leibert_t_Page_061.jp2
497499 F20101129_AAAXDE leibert_t.pdf
F20101129_AAAXCQ leibert_t_Page_109.tif
91643 F20101129_AAAWXK leibert_t_Page_078.jp2
111149 F20101129_AAAWWW leibert_t_Page_062.jp2
6587 F20101129_AAAXDF leibert_t_Page_030thm.jpg
F20101129_AAAXCR leibert_t_Page_110.tif
107205 F20101129_AAAWXL leibert_t_Page_079.jp2
110779 F20101129_AAAWWX leibert_t_Page_063.jp2
26499 F20101129_AAAXDG leibert_t_Page_125.QC.jpg
107892 F20101129_AAAWYA leibert_t_Page_096.jp2
F20101129_AAAXCS leibert_t_Page_112.tif
111150 F20101129_AAAWWY leibert_t_Page_065.jp2
5311 F20101129_AAAXDH leibert_t_Page_056thm.jpg
103649 F20101129_AAAWYB leibert_t_Page_097.jp2
F20101129_AAAXCT leibert_t_Page_114.tif
91780 F20101129_AAAWXM leibert_t_Page_080.jp2
107743 F20101129_AAAWWZ leibert_t_Page_066.jp2
22884 F20101129_AAAXDI leibert_t_Page_042.QC.jpg
111334 F20101129_AAAWYC leibert_t_Page_098.jp2
F20101129_AAAXCU leibert_t_Page_116.tif
99643 F20101129_AAAWXN leibert_t_Page_081.jp2
23150 F20101129_AAAXDJ leibert_t_Page_045.QC.jpg
111279 F20101129_AAAWYD leibert_t_Page_099.jp2
F20101129_AAAXCV leibert_t_Page_117.tif
11900 F20101129_AAAWXO leibert_t_Page_082.jp2
5775 F20101129_AAAXDK leibert_t_Page_129thm.jpg
34766 F20101129_AAAWYE leibert_t_Page_100.jp2
92210 F20101129_AAAWXP leibert_t_Page_083.jp2
18681 F20101129_AAAXDL leibert_t_Page_072.QC.jpg
884229 F20101129_AAAWYF leibert_t_Page_101.jp2
F20101129_AAAXCW leibert_t_Page_119.tif
111774 F20101129_AAAWXQ leibert_t_Page_084.jp2
2772 F20101129_AAAXDM leibert_t_Page_100thm.jpg
1051983 F20101129_AAAWYG leibert_t_Page_102.jp2
F20101129_AAAXCX leibert_t_Page_121.tif
113372 F20101129_AAAWXR leibert_t_Page_085.jp2
5341 F20101129_AAAXEA leibert_t_Page_101thm.jpg
6652 F20101129_AAAXDN leibert_t_Page_122thm.jpg
61125 F20101129_AAAWYH leibert_t_Page_103.jp2
F20101129_AAAXCY leibert_t_Page_123.tif
108644 F20101129_AAAWXS leibert_t_Page_087.jp2
6120 F20101129_AAAXEB leibert_t_Page_071thm.jpg
6550 F20101129_AAAXDO leibert_t_Page_090thm.jpg
53204 F20101129_AAAWYI leibert_t_Page_104.jp2
F20101129_AAAXCZ leibert_t_Page_124.tif
105998 F20101129_AAAWXT leibert_t_Page_089.jp2
23597 F20101129_AAAXEC leibert_t_Page_044.QC.jpg
23820 F20101129_AAAXDP leibert_t_Page_066.QC.jpg
23673 F20101129_AAAWYJ leibert_t_Page_105.jp2
112495 F20101129_AAAWXU leibert_t_Page_090.jp2
23836 F20101129_AAAXED leibert_t_Page_047.QC.jpg
6687 F20101129_AAAXDQ leibert_t_Page_033thm.jpg
30240 F20101129_AAAWYK leibert_t_Page_106.jp2
110843 F20101129_AAAWXV leibert_t_Page_091.jp2
7656 F20101129_AAAXEE leibert_t_Page_111.QC.jpg
23797 F20101129_AAAXDR leibert_t_Page_029.QC.jpg
952052 F20101129_AAAWYL leibert_t_Page_108.jp2
111411 F20101129_AAAWXW leibert_t_Page_092.jp2
5949 F20101129_AAAXEF leibert_t_Page_006thm.jpg
16466 F20101129_AAAWZA leibert_t_Page_128.jp2
21412 F20101129_AAAXDS leibert_t_Page_081.QC.jpg
710577 F20101129_AAAWYM leibert_t_Page_109.jp2
109574 F20101129_AAAWXX leibert_t_Page_093.jp2
6179 F20101129_AAAXEG leibert_t_Page_097thm.jpg
90093 F20101129_AAAWZB leibert_t_Page_129.jp2
24520 F20101129_AAAXDT leibert_t_Page_060.QC.jpg
112708 F20101129_AAAWXY leibert_t_Page_094.jp2
6611 F20101129_AAAXEH leibert_t_Page_029thm.jpg
F20101129_AAAWZC leibert_t_Page_001.tif
6554 F20101129_AAAXDU leibert_t_Page_013thm.jpg
48116 F20101129_AAAWYN leibert_t_Page_110.jp2
113887 F20101129_AAAWXZ leibert_t_Page_095.jp2
6344 F20101129_AAAXEI leibert_t_Page_046thm.jpg
F20101129_AAAWZD leibert_t_Page_002.tif
24242 F20101129_AAAXDV leibert_t_Page_030.QC.jpg
33715 F20101129_AAAWYO leibert_t_Page_111.jp2
6676 F20101129_AAAXEJ leibert_t_Page_049thm.jpg
F20101129_AAAWZE leibert_t_Page_003.tif
5801 F20101129_AAAXDW leibert_t_Page_080thm.jpg
117248 F20101129_AAAWYP leibert_t_Page_112.jp2
21471 F20101129_AAAXEK leibert_t_Page_011.QC.jpg
F20101129_AAAWZF leibert_t_Page_005.tif
136296 F20101129_AAAWYQ leibert_t_Page_113.jp2
6437 F20101129_AAAXEL leibert_t_Page_038thm.jpg
F20101129_AAAWZG leibert_t_Page_006.tif
1896 F20101129_AAAXDX leibert_t_Page_026thm.jpg
132466 F20101129_AAAWYR leibert_t_Page_115.jp2
23436 F20101129_AAAXFA leibert_t_Page_049.QC.jpg
5108 F20101129_AAAXEM leibert_t_Page_010thm.jpg
F20101129_AAAWZH leibert_t_Page_007.tif
6801 F20101129_AAAXDY leibert_t_Page_118thm.jpg
1051959 F20101129_AAAWYS leibert_t_Page_116.jp2
22620 F20101129_AAAXFB leibert_t_Page_112.QC.jpg
6462 F20101129_AAAXEN leibert_t_Page_017thm.jpg
F20101129_AAAWZI leibert_t_Page_008.tif
6178 F20101129_AAAXDZ leibert_t_Page_023thm.jpg
133905 F20101129_AAAWYT leibert_t_Page_117.jp2
F20101129_AAAXFC leibert_t_Page_043.QC.jpg
23850 F20101129_AAAXEO leibert_t_Page_022.QC.jpg
F20101129_AAAWZJ leibert_t_Page_009.tif
132757 F20101129_AAAWYU leibert_t_Page_118.jp2
6407 F20101129_AAAXFD leibert_t_Page_063thm.jpg
11639 F20101129_AAAXEP leibert_t_Page_104.QC.jpg
F20101129_AAAWZK leibert_t_Page_010.tif
138414 F20101129_AAAWYV leibert_t_Page_120.jp2
6101 F20101129_AAAXFE leibert_t_Page_077thm.jpg
7439 F20101129_AAAXEQ leibert_t_Page_127thm.jpg
F20101129_AAAWZL leibert_t_Page_011.tif
120140 F20101129_AAAWYW leibert_t_Page_122.jp2
6470 F20101129_AAAXFF leibert_t_Page_015thm.jpg
6446 F20101129_AAAXER leibert_t_Page_098thm.jpg
F20101129_AAAWZM leibert_t_Page_012.tif
143611 F20101129_AAAWYX leibert_t_Page_125.jp2
24183 F20101129_AAAXFG leibert_t_Page_064.QC.jpg
6704 F20101129_AAAXES leibert_t_Page_022thm.jpg
F20101129_AAAWZN leibert_t_Page_014.tif
131526 F20101129_AAAWYY leibert_t_Page_126.jp2
23407 F20101129_AAAXFH leibert_t_Page_086.QC.jpg
6549 F20101129_AAAXET leibert_t_Page_032thm.jpg
1051949 F20101129_AAAWYZ leibert_t_Page_127.jp2
6397 F20101129_AAAXFI leibert_t_Page_041thm.jpg
6592 F20101129_AAAXEU leibert_t_Page_043thm.jpg
F20101129_AAAWZO leibert_t_Page_015.tif
4564 F20101129_AAAXFJ leibert_t_Page_070thm.jpg
6323 F20101129_AAAXEV leibert_t_Page_058thm.jpg
F20101129_AAAWZP leibert_t_Page_016.tif
23610 F20101129_AAAXFK leibert_t_Page_038.QC.jpg
1816 F20101129_AAAXEW leibert_t_Page_128thm.jpg
F20101129_AAAWZQ leibert_t_Page_018.tif
22139 F20101129_AAAXFL leibert_t_Page_050.QC.jpg
6427 F20101129_AAAXEX leibert_t_Page_047thm.jpg
F20101129_AAAWZR leibert_t_Page_019.tif
3128 F20101129_AAAXGA leibert_t_Page_008thm.jpg
6279 F20101129_AAAXFM leibert_t_Page_096thm.jpg
F20101129_AAAWZS leibert_t_Page_021.tif
5084 F20101129_AAAXGB leibert_t_Page_009thm.jpg
6697 F20101129_AAAXFN leibert_t_Page_051thm.jpg
6675 F20101129_AAAXEY leibert_t_Page_119thm.jpg
F20101129_AAAWZT leibert_t_Page_022.tif
17775 F20101129_AAAXGC leibert_t_Page_010.QC.jpg
141562 F20101129_AAAXFO UFE0011283_00001.xml
6878 F20101129_AAAXEZ leibert_t_Page_126thm.jpg
F20101129_AAAWZU leibert_t_Page_024.tif
23142 F20101129_AAAXGD leibert_t_Page_012.QC.jpg
3314 F20101129_AAAXFP leibert_t_Page_002.QC.jpg
24335 F20101129_AAAXGE leibert_t_Page_013.QC.jpg
1382 F20101129_AAAXFQ leibert_t_Page_002thm.jpg
F20101129_AAAWZV leibert_t_Page_025.tif
22495 F20101129_AAAXGF leibert_t_Page_015.QC.jpg
20463 F20101129_AAAXFR leibert_t_Page_003.QC.jpg
F20101129_AAAWZW leibert_t_Page_026.tif
6403 F20101129_AAAXGG leibert_t_Page_016thm.jpg
5888 F20101129_AAAXFS leibert_t_Page_003thm.jpg
F20101129_AAAWZX leibert_t_Page_027.tif
22761 F20101129_AAAXGH leibert_t_Page_017.QC.jpg
8944 F20101129_AAAXFT leibert_t_Page_004.QC.jpg
F20101129_AAAWZY leibert_t_Page_028.tif
6511 F20101129_AAAXGI leibert_t_Page_018thm.jpg
2717 F20101129_AAAXFU leibert_t_Page_004thm.jpg
F20101129_AAAWZZ leibert_t_Page_029.tif
6562 F20101129_AAAXGJ leibert_t_Page_021thm.jpg
16195 F20101129_AAAXFV leibert_t_Page_005.QC.jpg
20729 F20101129_AAAXGK leibert_t_Page_023.QC.jpg
4487 F20101129_AAAXFW leibert_t_Page_005thm.jpg
22531 F20101129_AAAXGL leibert_t_Page_025.QC.jpg
23075 F20101129_AAAXFX leibert_t_Page_006.QC.jpg
6247 F20101129_AAAXGM leibert_t_Page_025thm.jpg
6713 F20101129_AAAXFY leibert_t_Page_007.QC.jpg
23128 F20101129_AAAXHA leibert_t_Page_039.QC.jpg
5213 F20101129_AAAXGN leibert_t_Page_026.QC.jpg
22736 F20101129_AAAXHB leibert_t_Page_040.QC.jpg
21064 F20101129_AAAXGO leibert_t_Page_027.QC.jpg
10176 F20101129_AAAXFZ leibert_t_Page_008.QC.jpg
23638 F20101129_AAAXHC leibert_t_Page_041.QC.jpg
24002 F20101129_AAAXGP leibert_t_Page_028.QC.jpg
6320 F20101129_AAAXHD leibert_t_Page_042thm.jpg
6631 F20101129_AAAXGQ leibert_t_Page_028thm.jpg
23543 F20101129_AAAXHE leibert_t_Page_048.QC.jpg
23170 F20101129_AAAXGR leibert_t_Page_031.QC.jpg
6514 F20101129_AAAXHF leibert_t_Page_048thm.jpg
6293 F20101129_AAAXGS leibert_t_Page_031thm.jpg
6337 F20101129_AAAXHG leibert_t_Page_050thm.jpg
24432 F20101129_AAAXHH leibert_t_Page_051.QC.jpg
23732 F20101129_AAAXGT leibert_t_Page_032.QC.jpg
22369 F20101129_AAAXHI leibert_t_Page_052.QC.jpg
23424 F20101129_AAAXGU leibert_t_Page_034.QC.jpg
6408 F20101129_AAAXHJ leibert_t_Page_052thm.jpg
21856 F20101129_AAAXGV leibert_t_Page_035.QC.jpg
23146 F20101129_AAAXHK leibert_t_Page_053.QC.jpg
6319 F20101129_AAAXGW leibert_t_Page_035thm.jpg
6498 F20101129_AAAXHL leibert_t_Page_053thm.jpg
24023 F20101129_AAAXGX leibert_t_Page_036.QC.jpg
6199 F20101129_AAAXIA leibert_t_Page_067thm.jpg
6716 F20101129_AAAXHM leibert_t_Page_054thm.jpg
6780 F20101129_AAAXGY leibert_t_Page_036thm.jpg
17933 F20101129_AAAXIB leibert_t_Page_068.QC.jpg
22096 F20101129_AAAXHN leibert_t_Page_055.QC.jpg
23582 F20101129_AAAXGZ leibert_t_Page_037.QC.jpg
5009 F20101129_AAAXIC leibert_t_Page_068thm.jpg
6246 F20101129_AAAXHO leibert_t_Page_055thm.jpg
19564 F20101129_AAAXID leibert_t_Page_069.QC.jpg
21553 F20101129_AAAXHP leibert_t_Page_057.QC.jpg
5513 F20101129_AAAXIE leibert_t_Page_069thm.jpg
6127 F20101129_AAAXHQ leibert_t_Page_057thm.jpg
21905 F20101129_AAAXIF leibert_t_Page_071.QC.jpg
22804 F20101129_AAAXHR leibert_t_Page_058.QC.jpg
5360 F20101129_AAAXIG leibert_t_Page_072thm.jpg
24980 F20101129_AAAXHS leibert_t_Page_059.QC.jpg
6230 F20101129_AAAXIH leibert_t_Page_073thm.jpg
6899 F20101129_AAAXHT leibert_t_Page_059thm.jpg
6275 F20101129_AAAXII leibert_t_Page_074thm.jpg
6235 F20101129_AAAXHU leibert_t_Page_061thm.jpg
23415 F20101129_AAAXIJ leibert_t_Page_075.QC.jpg
6473 F20101129_AAAXHV leibert_t_Page_062thm.jpg
6346 F20101129_AAAXIK leibert_t_Page_075thm.jpg
23593 F20101129_AAAXHW leibert_t_Page_063.QC.jpg
19552 F20101129_AAAXIL leibert_t_Page_076.QC.jpg
6679 F20101129_AAAXHX leibert_t_Page_064thm.jpg
5575 F20101129_AAAXIM leibert_t_Page_076thm.jpg
6683 F20101129_AAAXHY leibert_t_Page_066thm.jpg
6589 F20101129_AAAXJA leibert_t_Page_088thm.jpg
21541 F20101129_AAAXIN leibert_t_Page_077.QC.jpg
21956 F20101129_AAAXHZ leibert_t_Page_067.QC.jpg
22842 F20101129_AAAXJB leibert_t_Page_089.QC.jpg
5944 F20101129_AAAXIO leibert_t_Page_078thm.jpg
24617 F20101129_AAAXJC leibert_t_Page_090.QC.jpg
20263 F20101129_AAAXIP leibert_t_Page_080.QC.jpg
23634 F20101129_AAAXJD leibert_t_Page_091.QC.jpg
5809 F20101129_AAAXIQ leibert_t_Page_081thm.jpg
6477 F20101129_AAAXJE leibert_t_Page_091thm.jpg
4568 F20101129_AAAXIR leibert_t_Page_082.QC.jpg
24189 F20101129_AAAXJF leibert_t_Page_092.QC.jpg
1685 F20101129_AAAXIS leibert_t_Page_082thm.jpg
6653 F20101129_AAAXJG leibert_t_Page_092thm.jpg
19933 F20101129_AAAXIT leibert_t_Page_083.QC.jpg
23229 F20101129_AAAXJH leibert_t_Page_093.QC.jpg
5659 F20101129_AAAXIU leibert_t_Page_083thm.jpg
F20101129_AAAXJI leibert_t_Page_093thm.jpg
23761 F20101129_AAAXIV leibert_t_Page_084.QC.jpg
6586 F20101129_AAAXJJ leibert_t_Page_094thm.jpg
6493 F20101129_AAAXIW leibert_t_Page_084thm.jpg
F20101129_AAAXJK leibert_t_Page_095.QC.jpg
6548 F20101129_AAAXIX leibert_t_Page_085thm.jpg
23356 F20101129_AAAXJL leibert_t_Page_096.QC.jpg
23042 F20101129_AAAXIY leibert_t_Page_087.QC.jpg
3347 F20101129_AAAXKA leibert_t_Page_110thm.jpg
21810 F20101129_AAAXJM leibert_t_Page_097.QC.jpg
6530 F20101129_AAAXIZ leibert_t_Page_087thm.jpg
2557 F20101129_AAAXKB leibert_t_Page_111thm.jpg
6615 F20101129_AAAXJN leibert_t_Page_099thm.jpg
6108 F20101129_AAAXKC leibert_t_Page_112thm.jpg
8931 F20101129_AAAXJO leibert_t_Page_100.QC.jpg
25765 F20101129_AAAXKD leibert_t_Page_113.QC.jpg
19039 F20101129_AAAXJP leibert_t_Page_101.QC.jpg
6919 F20101129_AAAXKE leibert_t_Page_113thm.jpg
24363 F20101129_AAAXJQ leibert_t_Page_102.QC.jpg
26610 F20101129_AAAXKF leibert_t_Page_114.QC.jpg
6632 F20101129_AAAXJR leibert_t_Page_102thm.jpg
7009 F20101129_AAAXKG leibert_t_Page_114thm.jpg
4167 F20101129_AAAXJS leibert_t_Page_103thm.jpg
24909 F20101129_AAAXKH leibert_t_Page_115.QC.jpg
3580 F20101129_AAAXJT leibert_t_Page_104thm.jpg
6742 F20101129_AAAXKI leibert_t_Page_115thm.jpg
2420 F20101129_AAAXJU leibert_t_Page_105thm.jpg
7170 F20101129_AAAXKJ leibert_t_Page_116thm.jpg
2540 F20101129_AAAXJV leibert_t_Page_106thm.jpg
24939 F20101129_AAAXKK leibert_t_Page_117.QC.jpg
6507 F20101129_AAAXJW leibert_t_Page_107.QC.jpg
24769 F20101129_AAAXKL leibert_t_Page_119.QC.jpg
17165 F20101129_AAAXJX leibert_t_Page_109.QC.jpg
6813 F20101129_AAAXKM leibert_t_Page_120thm.jpg
4932 F20101129_AAAXJY leibert_t_Page_109thm.jpg
22668 F20101129_AAAXKN leibert_t_Page_122.QC.jpg
11111 F20101129_AAAXJZ leibert_t_Page_110.QC.jpg
25519 F20101129_AAAXKO leibert_t_Page_123.QC.jpg
6897 F20101129_AAAXKP leibert_t_Page_123thm.jpg
25547 F20101129_AAAXKQ leibert_t_Page_124.QC.jpg
6633 F20101129_AAAXKR leibert_t_Page_124thm.jpg
7094 F20101129_AAAXKS leibert_t_Page_125thm.jpg
24944 F20101129_AAAXKT leibert_t_Page_126.QC.jpg
27769 F20101129_AAAXKU leibert_t_Page_127.QC.jpg
5207 F20101129_AAAXKV leibert_t_Page_128.QC.jpg
2264 F20101129_AAAXKW leibert_t_Page_130thm.jpg
25004 F20101129_AAAWMA leibert_t_Page_118.QC.jpg
87626 F20101129_AAAWMB leibert_t_Page_102.jpg
2085 F20101129_AAAWMC leibert_t_Page_107thm.jpg
F20101129_AAAWMD leibert_t_Page_069.tif
5687 F20101129_AAAWME leibert_t_Page_024thm.jpg
F20101129_AAAWMF leibert_t_Page_115.tif
71259 F20101129_AAAWMG leibert_t_Page_031.jpg
6334 F20101129_AAAWLS leibert_t_Page_045thm.jpg
F20101129_AAAWMH leibert_t_Page_020.tif
66448 F20101129_AAAWLT leibert_t_Page_011.jpg
F20101129_AAAWMI leibert_t_Page_014thm.jpg
62788 F20101129_AAAWLU leibert_t_Page_078.jpg
F20101129_AAAWMJ leibert_t_Page_076.tif
F20101129_AAAWLV leibert_t_Page_037thm.jpg
F20101129_AAAWMK leibert_t_Page_017.tif
23952 F20101129_AAAWLW leibert_t_Page_062.QC.jpg
70886 F20101129_AAAWNA leibert_t_Page_039.jpg
F20101129_AAAWML leibert_t_Page_023.tif
F20101129_AAAWLX leibert_t_Page_125.tif
F20101129_AAAWNB leibert_t_Page_036.tif
21784 F20101129_AAAWMM leibert_t_Page_108.QC.jpg
109025 F20101129_AAAWLY leibert_t_Page_053.jp2
23730 F20101129_AAAWMN leibert_t_Page_098.QC.jpg
72063 F20101129_AAAWLZ leibert_t_Page_018.jpg
24112 F20101129_AAAWMO leibert_t_Page_094.QC.jpg
67228 F20101129_AAAWNC leibert_t_Page_097.jpg
77588 F20101129_AAAWMP leibert_t_Page_122.jpg
F20101129_AAAWND leibert_t_Page_004.tif
23711 F20101129_AAAWMQ leibert_t_Page_065.QC.jpg
F20101129_AAAWNE leibert_t_Page_039.tif
103708 F20101129_AAAWMR leibert_t_Page_035.jp2
18544 F20101129_AAAWNF leibert_t_Page_024.QC.jpg
6328 F20101129_AAAWMS leibert_t_Page_089thm.jpg
6572 F20101129_AAAWNG leibert_t_Page_095thm.jpg
87126 F20101129_AAAWMT leibert_t_Page_113.jpg
130831 F20101129_AAAWNH leibert_t_Page_119.jp2
23607 F20101129_AAAWMU leibert_t_Page_099.QC.jpg
20338 F20101129_AAAWNI leibert_t_Page_078.QC.jpg
23290 F20101129_AAAWMV leibert_t_Page_014.QC.jpg
F20101129_AAAWNJ leibert_t_Page_118.tif
72312 F20101129_AAAWMW leibert_t_Page_043.jpg
23900 F20101129_AAAWNK leibert_t_Page_107.jp2
109274 F20101129_AAAWMX leibert_t_Page_031.jp2
F20101129_AAAWOA leibert_t_Page_083.tif
6595 F20101129_AAAWNL leibert_t_Page_020thm.jpg
26500 F20101129_AAAWMY leibert_t_Page_004.jpg
111396 F20101129_AAAWOB leibert_t_Page_086.jp2
70821 F20101129_AAAWNM leibert_t_Page_017.jpg
137337 F20101129_AAAWMZ leibert_t_Page_123.jp2
6418 F20101129_AAAWOC leibert_t_Page_039thm.jpg
24035 F20101129_AAAWNN leibert_t_Page_021.QC.jpg
71170 F20101129_AAAWNO leibert_t_Page_087.jpg




Copyright 2005 by Todd W. Leibert


iii ACKNOWLEDGMENTS First and foremost, I must thank my gi rlfriend, Lyanna Doxey, for her unwavering conviction that I should pursue a PhD despite my many reservations. Her support was the one constant in my life through to the conclusi on of my dissertation. I am grateful that she withstood her own aversion to dealing with life’s uncertainties in order that I fulfill my own potentials in life. I will be forever th ankful for her foresight, support, and love. I want to extend my gratitude towards my doctoral chair, James Archer, Jr., for his efforts to help me complete my disserta tion despite many unexpected personal hardships he faced. He conveyed a deep respect for my ability to thrive as a professor and researcher. He lived up to his billing as a fast editor and greatly facilitated the completion of my dissertation and bo lstered my confidence. I am also appreciative of the rest of my committee, Harry Daniels, Ellen Amatea, and Jamie Algina, for their competent, skil lful, insightful, encouraging input. Each member provided unique help that I think help shaped my development in the PhD program and on the dissertation. I am indebted to each member for their contributions. I want to thank my parents, Bob and Dixi e, for their emotiona l support and interest in my progression. I thank my father, whose many years as a university professor led to wise and experienced advice that saved me from making the pro cess harder than it needed to be. I want to express my thanks to peers Jenny Bergeron, for her competent statistical consults; Tim Baker, for his assistance and use of the online survey software; Kelly


iv Morgan, for her timely medica l help; and Stephanie Carter for helping me with the formatting of this document, my most fear ed element of the whole dissertation. I am grateful to the many message board sites that supported my study and the people who were willing to complete my survey. The study could not have been completed without their assistance. Finally, my cat’s efforts should not go unsung. Kito provided diligent oversight in my keyboard operations and attack ed research articles with a fervor even I could not attain.


v TABLE OF CONTENTS page ACKNOWLEDGMENTS.................................................................................................iii LIST OF TABLES...........................................................................................................viii ABSTRACT....................................................................................................................... ix CHAPTER 1 INTRODUCTION........................................................................................................1 Theoretical Framework.................................................................................................7 Statement of the Problem............................................................................................10 Need for the Study......................................................................................................11 Purpose of the Study...................................................................................................12 Rationale for the Study...............................................................................................12 Research Questions.....................................................................................................13 Definition of Terms....................................................................................................13 Organization of the Study...........................................................................................15 2 REVIEW OF LITERATURE.....................................................................................17 Social Support.............................................................................................................19 Perceived Criticism.....................................................................................................22 Motivation...................................................................................................................26 Expectancy/Hope........................................................................................................28 Physical Health...........................................................................................................31 SES and Major Role Satisfaction...............................................................................33 Complexity/Chronicity of Problems...........................................................................36 Sociodemographic Factors..........................................................................................38 Response Rate.............................................................................................................39 General Conclusion....................................................................................................44 3 METHODOLOGY.....................................................................................................46 Statement of Purpose..................................................................................................46 Hypotheses..................................................................................................................46 Descriptions of Variables...........................................................................................47 Dependent Variables...........................................................................................47


vi Independent Variables.........................................................................................47 Population...................................................................................................................48 Sampling Procedures..................................................................................................48 Data Collection Procedures........................................................................................50 Instrumentation...........................................................................................................51 Outcome Questionnaire-45 (OQ-45)...................................................................51 Duke Social Support Questionnaire (DSSQ)......................................................53 Social Adjustment Scale-Self Report (SAS-SR).................................................54 Perceived Criticism Measure (PCM)...................................................................54 University of Rhode Island Change Assessment (URICA Long Form).............55 Treatment Expectancy Scale (TES).....................................................................56 Health Survey Short-Form-12 (SF-12)................................................................57 Single Item Questions..........................................................................................58 Data Analysis..............................................................................................................58 4 DATA ANALYSIS....................................................................................................59 Data Collection and Descriptive Statistics.................................................................59 Decision Rule......................................................................................................62 Reliability Estimates for OQ-45, DSSQ, and URICA........................................63 Test of Assumptions, Multicollin earity, and Undue Influence...........................63 Correlation Analysis............................................................................................64 Regression Analysis............................................................................................66 Nature of the Outliers and Rationale for Outlier Exclusion................................68 Hypothesis Testing.....................................................................................................69 Chapter Summary.......................................................................................................71 5 SUMMARY, CONCLUSIONS AND RECOMMENDATIONS.............................73 Summary of the Study................................................................................................73 Conclusions.................................................................................................................74 Limitations..................................................................................................................86 Implications and Recommendations...........................................................................87 Research..............................................................................................................87 Clinical................................................................................................................89 APPENDIX A ADVERTISEMENT AND CONSENT......................................................................91 B DUKES SOCIAL SUPPORT QUESTIONNAIRE....................................................93 C PERCEIVED CRITICISM SCALE...........................................................................95 D SOCIAL ADJUSTMENT SCALE-SELF REPORT (SAS-SR).................................96 E TREATMENT EXPECTANCY SCALE...................................................................97


vii F URICA (LONG FORM) UNIVERSITY OF RHODE ISLAND CHANGE ASSESSMENT...........................................................................................................98 G CLIENT SOCIODEMOGRAPHI C INFORMATION (9-ITEMS)..........................100 REFERENCE LIST.........................................................................................................102 BIOGRAPHICAL SKETCH...........................................................................................119


viii LIST OF TABLES Table page 1 Frequency Distribution of Participat ing Mental Health Message Boards................60 2 Descriptive Statistics for the Continuous Variables.................................................61 3 Recoding Reports of Time or Sessions in Counseling.............................................62 4 Zero-Order Correlations between Client Variable and the OQ-45..........................65 5 Source Table for the Model of Client Variables on the OQ-45...............................66 6 Regression Coefficient Estimat es and Independent Variance..................................67 7 Client model and Individual Client Factor Semi-Partial Correlations.....................68


ix 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 RELATIONSHIP BETWEEN CLIENT FA CTORS AND SYMPTOM LEVELS FOR CLIENTS IN ONGOING MENTAL HEALTH TREATMENT By Todd Leibert August 2005 Chair: James Archer Major Department: Counselor Education The common factors model of mental health counseling posits that as much as 55% of the variance in outcome results from client factors apart from th e counseling itself. The primary purpose of this study was to evalua te the relationship of common factors on client symptom levels during an ongoing treat ment attempt. The secondary purpose of the study was to assess which client factors were associated with positive mental health symptom levels. Based on counseling literature, 13 client factors we re selected on the basis of their prognostic potential. The dependent variable was the Outcome Questionnaire-45 which provided a global measur e of mental health comprised of the three subscales symptom distress, quality of interpersonal relations, and social role at home, work, or school. Clients undergoing mental health counseli ng were solicited thr ough Internet selfhelp mental health message/bulletin boards th at primarily targeted depression and anxiety related concerns. Recruitment of participants began by first achieving access to Internet


x self-help message/bulletin boards and then pos ting an advertisement directed towards members also involved in face-to-face profe ssional mental health counseling. Seventyfour mental health message/bulletin board fo rums were solicited for participation over a period of 13 weeks. In total, 195 self-selec ted volunteer responses to the survey were analyzed using correlation and multiple regression analyses. Respondents were mainly adult, white, well-educated females, with a history of chronic and severe mental health concerns. The results revealed that the 13 client factors measured significantly related to client sy mptom level and collect ively explained over half (i.e., 58%) of the variance in client sy mptom level, thus supporting the influence of the common factors model for clients undergoi ng mental health treatment. Satisfaction with social supports and prim ary life role (e.g., student, re tired volunteer) were the two most influential factors relating to less repor ted symptom levels. Fewer prior attempts at coping with the respondent’s presenting probl em, higher education level, satisfaction with physical health, older age, and to a lesse r extent, financial secu rity, no incidence of emotional or sexual abuse, and hope/expectancy for improvement, also related to reports of fewer mental health symp toms. Clinical implications and future research are elucidated.


1 CHAPTER 1 INTRODUCTION Mental health therapists face increasing pr essures to demonstrate effective practice by third-party payers, health managed organi zations (HMOs), and administrators (Addis, Wade, & Hatgis, 1999; Plante, Couchman, & Hoffman, 1998; Rainer, 1996). The now infamous Hans Eysenck (1952) study purpor ting that people undergoing psychotherapy were no better off than those going without psychotherapy galvanized researchers to focus on the task of outcome studies (Clark in & Levy, 2004). In the late 1970’s and early 1980’s, psychotherapy research ers subsequently turned in an impressive mass of empirical evidence demonstrating the general benefit of counseling (Shapiro & Shapiro, 1982; Smith & Glass, 1977; Smith, Glass, & Miller, 1980), and were able to conclude that clients who received counseling were be tter off than 80% of those who had not received counseling (Lambert & Ogles, 2004). With the general effectiveness of counse ling demonstrated, attempts were then undertaken to discover specifica lly what treatments, or scho ols of therapy, worked for which clients (Chambless & Ollendick, 2001), an d to identify what the curative factors (e.g., advanced empathy, disputing irrational beli efs) of therapy were (Lambert & Ogles, 2004). To accurately evaluate which schools of psychotherapy (e.g., cognitive-behavioral therapy vs. person centered therapy) were most effective, treatment manuals were developed that specified pro cedures for applying the inte rventions (Beck, Rush, Shaw, & Emery, 1979). The advent of treatment manuals -psychotherapy researchers constituted a “small revolution” (Luborsky & DuRubeis 1984) in counseling methodology. Manuals


2 also allowed researchers within a school to remove one intervention to an approach (i.e., dismantling study), or add one in tervention (i.e., component st udy) to that approach, to study which interventions were the crucial elements of the particular school of psychotherapy (Wampold, 2001). Paul’s overa rching question (1969) “What treatment, by whom, is most effective for this individua l with that specific problem, under which set of circumstances, and how does it come a bout?” was ready to be investigated. The advent of treatment manuals and, later, better measurement tools and statistical methods (Howard, Moras, Brill, Martinov ich, Lutz, 1996; Nathan, 1998), have indeed helped identify when specific treatments fo r diagnoses should be considered. Client ailments such as panic disorder, phobias, a nd compulsions have especially emerged as treatable interventions delineated in manua ls (Lambert & Ogles, 2004). Some findings even show that interventions from manuals improve therapy relations hips (Brown, Dreis, & Nace, 1999). Other reports indicate that manuals not only help clients with their primary problem (e.g., agoraphobia) but also with coexisting mental health complaints (Addis et al., 1999). Even though the rigor of comparative outcome studies in counseling research is in its infancy, in general, m odest advantages have accrued to behavioral, cognitive, or cognitive-behavioral approaches over humanistic approaches (Lambert & Ogles, 2004). These advances in the field are important as the healthcare cost-cutting policies of the 90s intensify demands on mental hea lth professionals to demonstrate success (Barlow, 1994; Hubble, Duncan, & Miller 1999a; Nathan, 1998). Mental health professionals responded to the demands by de veloping practice guidelines for the field (Nathan, 1998) and reporting 22 treatment s considered “empirically validated” by


3 Division 12 of Clinical Psychology of the Am erican Psychology Association (Division 12 Task Force, 1995). Although the Divisi on 12 psychotherapy researchers were attempting to build stability to the field, th eir publication was met with considerable resistance and controversy (Garfield, 1996; Silverman, 1996). Some researchers have provided evidence that a researcher’s trea tment allegiance, acting as a self-fulfilling prophecy, explained advantages accruing to em pirically validated treatments over other treatments (Robinson, Berman, & Neimeyer, 1990; Wampold, 2001). Meta-analyses (Wampold, Mondin, Moody, Stich, Benson, & Ahn, 1997) and null findings from studies comparing Empirically Supported Treatments against one another (ESTs; Elkin, Shea, Watkins, Imber, Sotsky, Collins, Glass et al ., 1989; Project Match Research Group, 1997) have led some research ers to invoke Rosenzwe ig’s (1936) classic analogy to Alice in Wonderland’s “dodo bi rd” verdict, where everyone wins and everyone gets prizes. In other words, all treatments work, and do so about equally well (Luborsky, Singer, & Luborsky, 1975; Stiles, Shapiro, & Elliott, 1986; Wampold et al., 1997). Although the dodo bird ve rdict has been disputed (Beutler, 2002; Chambless, 2002), others continue to espouse that nonspecific “ingredients” common to most psychotherapy approaches better explai n the data (Frank & Frank, 1991; Wampold, 2001). One model that has received much atte ntion in the field is outcome researcher Michael Lambert’s (1992) common factor model of therapy outcome. Based on his reviews of three decades of outcome researc h, and especially studi es about spontaneous remission of mental health symptoms (Lam bert, 1976), he estimated that the largest portion of outcome resulted from client vari ables. He theorized that 40% of outcome variance arose from “extratherapeutic” factors (i.e., factors outside the therapy room) and


4 another 15% proceeded from client expectancy for success in treatment. Thus, according to the theory, over half (55%) of what drives psychotherapy ch ange has little to do with what happens inside the therapy room. The ot her 45% of client change was speculated to result from the relationship between therapis t and client (accounti ng for 30% of change) and 15% depended on counseling school or approach. Although Lambert’s common factor model of outcome retains its adherents (Hubble et al., 1999a), the model has not been examined directly. Nor does the common factors model influence manage care policy. As outcome researcher Gene Glass (2001) colorfully expressed it: “any therapy that uses non-specifi c diagnoses and non-specific treatments is somehow bogus witchcraft lack ing indications of when to begin and when to end, and its application should be excluded from third-party cove rage” (p. x). Miller, Duncan, and Johnson (1999) contended that the inability of practitioners to show objective measures of their effectiveness will result in increasing forfeiture of control over their clinical work to “accountants and ac tuaries” (p.55). In actuality, third-party payers have assumed a larger role in dete rmining the type, frequency, and duration of treatment (Addis et al., 1999; Miller et al., 1999). The commo n factor model, in and of itself, has not helped reverse this trend. For researchers and practitioners wanting to demonstrate the efficacy of their approach without using manuals Howard et al.(1996) develo ped an alternative research method to accommodate HMOs in what is now called “Client-focused Research” (Whipple, Lambert, Vermeersch, Smart, Nielsen, & Hawkins, 2003). Client-focused research is a system for tracking and eval uating client progress during the treatment course for the purpose of improving services to the individual c lient (Lambert, 2001).


5 The focus is on the individual client while in treatment rather than groups of clients after treatment has been completed (Howard et al ., 1996). The tracking system of Howard et al. was derived from research about the dose-effect of psychotherapy based on multiple research efforts collected over 30 years and including 2,400 clients (Howard, Kopta, Krause, & Orlinsky, 1986). Howard et al. ( 1986) found that the grea test therapy gains came relatively fast in the first few couns eling sessions and that later gains came increasingly slower (i.e., negatively deceler ating change curve). Since that time, other research has replicated the negatively decel erating change curve in counseling outcomes (Lambert, Hansen, & Finch, 2001). Empirical be nchmarks provide a basis of comparison for whether treatment is working or not depending upon severity of problem and length of time in treatment. The benefits of client-focus ed research were describe d as “a win-win proposition,” not only for clinicians, but “for researchers, health care organizations and patients” alike (Lambert, 2001, p. 148). After all, clinicians want to use their preferred treatment approach and not necessarily an Empiri cally Supported Treatment (Silverman, 1996). The researchers are not restricted to time a nd cost intensive methods associated with controlling treatment methods to demonstrate empiri cal support of treatment. HMOs have a way to monitor and demonstrate cost-effectiv e treatment from their providers, without resorting to compliance checks and treatment plan updates (Brown et al., 1999; Miller et al., 1999). Finally, clients receive monitored care from clinician and HMO while getting to inform both parties of th eir progress on a regular basis. In short, client-focused research affords clinicians an alternative, practical way to demonstrate the scientific validity of their services.


6 Regardless of whether client-focused re search or manual-based research schemes have been utilized, client fact ors frequently emerge as mediating or moderating factors in what causes change in thera py (Clarkin & Levy, 2004; Sots ky, Glass, Shea, Pilkonis, Collins, Elkin, Watkins, et al., 1991). Manual studies that have shown a dose-effect response to treatment of diminishing returns have been used as further evidence of the influence of non-specific factors on outcome. Tallman and Bohart’s (1999) review of common factors concluded that change was a func tion of client attribut es and suggest that researchers “focus their study on clients rather than on therapists” (p. 199). In the most recent edition of the Handbook of Psychotherapy and Behavior Change chapter authors of “The Influence of Client Variables on Psychotherapy” concluded that studying client variables may greatly improve mental health professional’s unders tanding of counseling effectiveness. The guiding purpos e of their chapter was that “identification of premorbid clinical and personality characteristics predic tive of outcome might help clinicians guide treatment choices and revise treatment methods based on the needs of different types of clients” (Clarkin & Levy, 2004, p. 195). Thus, studying client factors more comprehensively has the potential to help the mental health counseling profession measure the role of treatment more precisely, improve delivery of therapy, and ultimately create more confidence of what counselors offer to themselves, funding bodies, and the public. A counseling maxim advises, that for best results, th e clinician should “go where the client is at.” The phras e captures the notion that a c lient is best served not by delivering the clinician’s preconc eived ideas of what a client needs, but by striving to discover and reflect as accurate ly as possible what the client asks for. Analogously, the


7 general aim of the present counseling outcome study is to listen to the accumulated voices of past research findings a nd include factors that have spoken the loudest on outcomes. The general purpose of this study is to examine c lient factors, or extr atherapeutic factors, that have shown the most promise in prior research, and evaluate their relationship to symptomatology for clients in ongoing counseling treatment. Theoretical Framework The theoretical framework for this study uses Lambert’s (1992) common factor model of therapy outcome. Because extrathera peutic factors (e.g., client social support, motivation, financial stability) are considered the greatest source for client outcome, this factor will be considered in the greatest deta il. Closely related to ex tratherapeutic factors is the client’s hope, or expectancy, for helpfu l treatment. Both of these factors originate with the client, and therefore, both of th ese factors will be considered together (theoretically, according to the common f actors model, accounting for 55% of the outcome variance in counseling treatment). Qualities that clients bring with them to the sessions are theorized to exert the greatest influence on how a treatment episod e ultimately goes (Lambert, 1992). After all, clients may only be in treatment for one hour a week, estimated at less than 1% of their waking hours (Whipple et al., 2003). Examples of extratherapeutic qualities are having a good home, financial stability, a loving famil y, caring friends, healt h, satisfaction with life, education, motivation, hope, chance life events, and a host of both developed and untapped innate abilities (Beutler & Clarkin, 1990; H ubble et al., 1999a; Lambert & Cattani-Thompson, 1996). The terms Social Support or Social Networ k refer to the clients perceptions about their interpersonal world: quality of family life, intimate relationships, friendships, and


8 the interactions among these networks. Social support indicates the adequacy to which clients are satisfied with their interpers onal relationships, and suggests how well the person will do in the interpersonal psychot herapy context. Clarkin and Levy (2004) conclude that “social suppor t is a summary statement about the interpersonal context within which the individual operates and ha s been found to be a potent variable in treatment outcomes” (p. 213). Because resear ch has consistently demonstrated that subjective perceptions of social support are more influential on psychotherapy treatment outcome than objective indicators of social support (e.g., number of friends, time spent with one’s social network, length of time in the relationship), this study will measure only subjective perceptions of social support. Stability and satisfaction with employmen t are regarded as critical to client response to treatment. As pointed out by Be utler and Clarkin ( 1990), the number of working hours frequently exceeds waking hours spent at home and likely plays a large role in maintaining ongoing mental health. Wo rk stress emerges when work roles are not clear, when there is interpersonal stress on th e job, and when feeling overwhelmed or not in control over work tasks (Fletcher & Payne, 1980; Holt, 1982; Karasek, 1979). Unemployment is related to psychological disturbance (Beu tler & Clarkin, 1990), and the greater work is desired while unemployed, th e greater the damage (Stafford, Jackson, & Banks, 1980; Warr, 1978). Adults attending school full time is assumed to play a similar role in mental health (Beutler & Clarkin, 1990) and is considered as part of the construct of employment in the pr esent investigation. In terms of motivation, this study draws on a transtheoretical model of change (Prochaska & DiClemente, 1983; Prochaska, 19 99) that explains how people modify or


9 alter behaviors whether in therapy or not. Acco rding to the model, ch ange is a function of five levels, or stages, of mo tivation an individual has towa rds a goal. The first stage, precontemplation, precedes serious thought of change; the second stage, contemplation, occurs when need for change becomes a cons ideration to think about; the third stage, preparation, describes that poi nt in time when steps are ta ken to get ready for making a change; the fourth stage, acti on, characterizes the physical laun ch into the change effort; the fifth stage, maintenance, is the degree to which the new behaviors are retained. A client’s stage of motivation is theoretically critical to amount and rate of change resulting from a treatment course. For the purposes of the present disserta tion, motivation will be tested for its relationship to sympto m level for clients during treatment. Hope/expectancy occurs when people believe they see one or more ways to achieve a goal and have the ability to initiate and pursue that goal (Snyder, Mi chael, & Cheavens, 1999). In the context of couns eling, Frank and Frank (1991) conceptualized clients entering treatment as individuals believing they have failed to resolve a problem and feel demoralized and powerless to solve the situation on their own. Clients see what must happen, but have lost their determination to pursue the goal. A first step in treatment is to return hope to the client (Snyder et al., 1999). Although, in the pres ent study, it was not possible to ascertain hope at the outset of treatment, participants were asked to estimate their pretreatment levels. Chronicity refers to other enduring problem s in other major life areas the client brings with them to the treatment epis ode. Chronic problems with mental health symptoms (Beutler, Clarkin, & Bongar, 2000), le gal, medical (Belsher & Costello, 1988; Murphy, 1983), employment (Gelhart, Ha nd-Ronga, & King, 2002; Mi-Young, 2001) or


10 financial areas (Chiesa, Drahorad, Longo, 2000) are all added complications that theoretically affect th e client’s response to treatmen t in psychotherapy. Chronic physical health problems reduce outcome and increase the risk for relapse (Belsher & Costello, 1988; Murphy, 1983). The term “response rate” refers to speed of progress made by a client during treatment. Concern over response rate rose as studies reported that average time clients spent in counseling was only between six to eight sessions (Beckham, 1989; Garfield, 1986). If clients did not respond rapidly to trea tment, then clients might not benefit from treatment. Identifying clients likely to fail w ithin the first couple of counseling sessions and discovering mechanisms for rapid impr ovement were considered important in improving psychotherapy treatment (Crits -Christoph, Connolly, Gallop, Barber, Tu, Gladis, & Siqueland, 2001; Wilson, Fairburn, Agras, Walsh, & Kraemer, 2002). The phenomenon of rapid response has been offe red as support for nonspecific or a common factors explanation for the active ingredient s of mental health counseling (Ilardi & Craighead, 1999; Tang & DuRubeis, 1999). De lineation of extratherapeutic and hope factors related to varying le vels of symptom severity dur ing counseling episodes might also help future researcher s identify an important source of variance in response rate. Statement of the Problem HMO’s have “challenged” the psycho therapy community to demonstrate comparative cost-effectiveness of different tr eatments in actual clin ical practice (Howard et al., 1996; Ogles, Lambert, & Fields, 2002). Ho wever, therapists as well as HMOs stand to profit from discovering the active ingred ients of treatment (Brown et al., 1999). Research aiming to identify client predictors for symptom amelioration will help clinicians adapt therapeutic approach to maximize outcome (Rude & Rehm, 1991). Rapid


11 early responses to treatment have eluded researcher attempts to identify specific ingredients and have suggested nonspecific (i.e., common fact ors) factors for the active ingredient behind their success (Ilardi & Crai ghead, 1999). In a commentary about extant knowledge of rapid response to treatment, W ilson (1999) suggested that “Finding robust, pretreatment predictors of treatment for a vari ety of clinical disorders remains a priority” (p.291). On the other hand, nonresponders to treatment also remain a puzzle. Approximately 10% of clients in counseli ng experience a worsening of symptoms (Bergin & Lambert, 1978; Mohr, 1995; Shap iro & Shapiro, 1982), while some 30-60% of clients drop out prematurely (Wierzbicki & Pekarik, 1993). Few empirical data are available to inform practitioners of whether de terioration in clients is a result of faulty treatment or particularly troubled clients (Mohr, Beu tler, Engle, Shoham-Salomon, Bergan, Kaszniak, & Yost, 1990). Not just a pr oblem for researchers, some studies show that therapists inaccurately predict whic h clients are likely to drop out (Auerbach, Luborsky, & Johnson, 1972; Lambert & Bergin, 1994; Lunnen & Ogles, 1998). The need for guidelines to determine when treatmen t should be altered has been called for (Auerbach et al., 1972; Wilson, 1999). Identifica tion of influential extratherapeutic and hope factors associated with be tter mental health symptom le vels might help researchers explain rapid responses and lack of responses to treatment. Need for the Study As reviewed by Clarkin and Levy (2004), mo st past research has endeavored to single out a central client variable predictive of outcome. They contended that “research focused on a constellation of salie nt variables will be likely to show the greatest impact on treatment process and outcome” (p. 215). Furt hermore, the majority of studies have used the medical model conception to study cl ient variables, relying on diagnosis and


12 purported treatment approaches. Clarkin and Le vy argue that the medical model leads to an oversimplification of the client variable and magnifies the separa tion between research and practice. The need, as they put forth, is to study an amalgam of non-diagnostic client variables based on theory and empirical findi ngs. In the past, such studies were often undertaken as an afterthought when the intended factors (esp ecially treatment approach) failed to produce positive impact on outcome (Clarkin & Levy, 2004). Finally, although studies using manuals typically attempt to c ontrol client variabilit y, client sources of variability are greater than that of treatment approach (Luborsky, McLellan, Woody, & O’Brien, 1985). A more producti ve avenue of research would be to explore how the diversity of client variables (i.e., extratherapeutic and hope factors) affects outcome. Purpose of the Study The purpose of this study is to evaluate the common factors extratherapeutic and hope factors relationship to sy mptom levels for ongoing mental health clients involved in Internet Mental Health Messa ge Boards. Using brief, clie nt-self-report measures, key client variables suggested by the common fact or theory of counseling will be examined for their relationship to persisting mental he alth symptoms. Results will be discussed in the context of improving theory and measurem ent of counseling, as well as implications for improving quality assurance in th e delivery of couns eling services. Rationale for the Study If therapy outcome is largely dependent on client variables, as the common factors model of therapy outcome proposes, the propos ition should be empirically testable by measuring the amount of variance client f actors account for in the symptom levels reported by clients during treatment. Results will help illuminate what client factors operate in the natural setting that relate to overall symptom levels. These results will also


13 help determine which moderating client variab les need to be accounted for when studying speed of response to treatment, as well as therapist and treatment manual contribution to outcome. If no clear results emerge, this will provide evidence against the common factor theory for counseling outcome. Null results might also suggest a general problem of measurement shortcoming in the field. In th at case, emphases should be placed heavier on outcome measure development. On the ot her hand, should client factors relate to symptom levels as expected, then the results will help shape future research about the role of professional c ounselors in helping clients and in what therapists offer to the public, themselves, and to funding bodies. The fo llowing questions were investigated in this study. Research Questions 1. What client factors predict lower levels of symptoms in clients during therapy treatment? 2. To what extent do client factors (extra therapeutic and hope) account for client symptom level in the course of an ongoing psychotherapy regimen? 3. Do the client factors explain over half of the treatment variance in symptom levels as would be suggested by the common factors model of treatment outcome? Definition of Terms Bulletin boards : Public forums that can be accessed through the Internet (e.g., Yahoo, Google, etc.) that allow people to join in online discussion about a given topic by reading previous messages from other members, adding your own, and receiving responses to your own messages. Used interchangeably with message boards. Client-focused research: A method for improving outcome for the individual client by empirically monitoring ongoing treatment response.


14 Common factor model : Treatment elements present in therapy across diverse approaches (i.e., extratherapeutic fact ors, empathy, relationship strength, hope, a treatment method). Dose-response : Refers to empirical findings in mental health counseling research showing that clients receive the greatest bene fits early in treatment and have diminishing returns as treatment continues. Empirically Supported Treatments (ESTs ): Therapy approaches that have shown statistical advantage over a comparison group (i.e., control group, wait list group, alternative treatment group) with a specific diagnostic clientele (i .e., major depression, agoraphobia). Extratherapeutic factors : Attributes about the client that exist in side and outside the therapy room (i.e., social supports, SE S, physical health, ego strength). Hope/expectancy factors : Sometimes referred to as plac ebo effects, refers to the client’s anticipation of improvement just by virtue of being treated by professional technique. Mental health message boards : Message boards that are specifically created for self-help regarding specified mental health concerns (e.g., eating disorders, grief). Message boards : Public forums that can be accessed through the Internet (e.g., Yahoo, Google, etc.) that allow people to join in online discussion about a given topic by reading previous messages from other members, adding your own, and receiving responses to your own messages. Used interchangeably with bulletin boards.


15 Negatively decelerating change curve : The belief that the greatest reduction of a client’s self-reported mental health symptoms occurs in th e first few counseling sessions; further improvements occur increasingly slower. Negative responder : A client whose outcome score (falls short of) the expected change score based on actuarial data (also called, slow/non responder). Positive responder : A client whose outcome score exceeds the expected change score based on actuarial data (a lso called, fast/rapid responder). Randomized Clinical Trials (RCTs) : A research method aiming to control extraneous factors influenc ing outcome by rigorously defi ning client selection and therapist treatment procedures (i.e., Therap ists are trained and monitored to follow a specific treatment manual; clie nts are selected according to a diagnosis to maximize their similarity to one another). Response rate: The speed of progress made by a c lient during treatment in terms of decreased level of symptoms as determin ed by the client’s self-report on a normed outcome measure. Organization of the Study This study is comprised of an abstract, five chapters, append ices, and a list of references. The abstract and chapter 1 present the history of establis hing the efficacy of counseling and modern methods to move the counseling field forward, including the value of investigating client variables on outcome. Chapter 2 reviews literature that supports variables germane to th is study: Client’s social s upport, perception of criticism, motivation for treatment, hope in treatment, life role satisfacti on, health, and factors related to SES. Chapter 3 addresses th e method, design, independent and dependent variables, and the statistical method to analy ze the results. Chapter 4 presents the results


16 of the statistical analysis. Chapter 5 su mmarizes the primary findings and their implications for both researchers and pract itioners. Finally, the appendices display measures that were used in this study.


17 CHAPTER 2 REVIEW OF LITERATURE This literature review will cover those clie nt factors cited frequently as important extratherapeutic variables affecting c ounseling treatment (Clarkin & Levy, 2004; Garfield, 1994). After a brief introduction to client factor s, literature on the following, client variables will be reviewed: social support, perceived criticism, motivation, expectancy/hope for treatment success, physical health, socioeconomic status (SES) and major role satisfaction, complexity/chronicity of the presenting problem for counseling including history of emotional or sexual abuse, and sociodemogr aphic factors (i.e., gender, age, education). Last, a more r ecent and sophisticated research methodology using client response rate will be discussed as further evidence for the importance of the client on treatment outcome. Research attempting to identify vital clie nt and expectancy/hope factors impacting counseling response for the purpose of guidi ng clinician choice of treatment method (Hoberman, Lewinsohn, & Tilson, 1988; Rounsaville, Weissman, & Prusoff, 1981) or understanding the wide varia tion in client response to treatment (Bosworth, Hays, George, & Steffens, 2002; Sotsky et al., 1991) have provided indirect support for a common factors model of psychotherapy outco me. Taken as a group, client factors tend to predict a substantial por tion of outcome variance. For example, in a study involving over 400 depressed clients starting counseling, Billings and Moos (1985) prospectively examined extratherapeutic factors, such as ongoing stressors, family and work environment, interpersonal relationships, a nd coping skills on recovery from depression


18 in counseling a year later. Extratherapeutic factors at posttreatment accounted for about 23% of the outcome variance in depression and when pretreatment depression levels were included, client variables expl ained approximately 45% of outcome variance. In another study, Steinmetz, Lewinsohn, and Antonuccio (1 983) carefully controlled for Depression level in a group counseling intervention to estimate impact of client factors and expectancy factors for improvement on depr ession. Client and expectancy factors accounted for roughly 25% of the outcome variance and when pretreatment levels of depression were included, about 50% of out come in depression was explained. In two smaller studies on depression treatment (N < 50) client factors explained between 6085% of outcome variance (Hoberman et al., 1988; Rounsaville et al., 1981). When the NIMH TDCRP study failed to find a superior treatment approach, the researchers retrospectively identifie d a handful of predictive client variables (Sotsky et al., 1991). Such research points to fact ors about the client as the key variable in explaining counseling outcome. Although substantial portio ns of variance have been accounted for by client factors and hope/expectancy across se veral studies, the precise influential client factors have been varied and in consistent. The goal of this literature review is to focus on the most consistent predictors of outcome. Several texts were used to help guide the search: The Handbook of Psychotherapy and Beha vior Change (Bergin & Garfield, 1994; Lambert, 2004), The Heart and Soul of Change (Hubble, Duncan, & Miller, 1999b), Systematic Treatment Selection: Toward Targ eted Therapeutic Interventions (Beutler & Clarkin, 1990), Guidelines for the Systema tic Treatment of the Depressed Patient (Beutler et al., 2000), and Who Will Benefit from Psychotherapy? Predicting Therapeutic Outcomes (Luborsky, Crits-Christo ph, Mintz, & Auerbach, 1988).


19 Social Support Many studies document a strong affect on counseling outcome depending upon the client’s social network. Billings and Moos (1985) conducted a 12-month prospective, longitudinal study of 380 adult depressed clients at six in-patient and out-patient clinics. The purpose was to determine the role of lif e stressors and social supports on various measures of outcome (e.g., depression, self -esteem). Seven indices of environmental stress (i.e., negative events, medical cond itions, spouse illness, children’s illness, negative home environment, and family argu ments) and six indice s for social support (i.e., number of friends, number of networ k contacts, number of close relationships, quality of significant personal relationship, family support, and work support) were used. The results showed that even when demogr aphics and intake de pression severity was taken into account (22.1 % of the outcome variance), both environmental stressors (12.8% of the outcome variance) and posttr eatment social supports (12.1% of the outcome variance) independently contribute d significantly to outcome (44.9 % total variance accounted for). Within the domain of environmental stressors predicting improvements on level of depression, all seven sub-indices had nearly equivalent partial correlations, ranging from .11 to .23. However, w ithin the domain of social support, the subjective indicators of social support were substantially greate r (range -.27 to -.39) than the objective indicators (range -.11 to -.17). Although Billings and Moos did not evaluate the strength of the 13 environment stressors and social support predictors against each other, it is interesting to note that the tw o largest environmenta l stressors were both related to subjective social supports: negative home environm ent and family arguments. One conclusion the researchers drew was th at subjective support was more important than objective indicators of support in predicting treatment outcome.


20 In a similar study, Moos (1990) explored th e extratherapeutic e ffects of stress and social supports on the success of treatment pr ograms on 265 depressed, outpatient clients. Negative stressors (e.g., medical conditions, fam ily conflict) in the client environment were evaluated both preceding and during treatment by clients. Quality of client confidants and family supports was also assesse d from client self-report. End of treatment analysis indicated that the two domains (i.e., social suppor t and environmental stress) together explained a significan t portion of variance in ame lioration of depression (3% at intake), and substantia lly more when measured 6 months later (14% of variance). The findings, though not as robust as found in B illings and Moos’ (1985) study, supported the importance of social support in pr omoting positive treatment outcome. The evidence for subjective social support as a prognosticator of favorable treatment outcome was also found in a NIMH Epidemiologic Catchment Area study involving 3,732 clients diagnosed as having symptoms of or having major depression (Landerman, George, Campbell, & Blazer, 1989). A more recent NIMH funded study that took into account of client social networ ks as part of the study question, essentially replicated the positive effect s of subjective social support (Bosworth et al., 2002). These two NIMH study results are also consistent with studies of people recovering from problems of habit. For example, a study exploring smoking-cessation among 46 participants with a si gnificant other found that clients su ccessfully abstinent six months posttreatment reported significantly greater perceived helpfuln ess from their significant others (Mermelstein, Lichtens tein, & McIntyre, 1983). In anot her example, evaluation of a weight loss program showed that percei ved support from friends and spouse was more predictive of successful weight loss at pretreatment than eith er expectations for weight


21 loss, % overweight, demographics, SES, or motivational factors (Prochaska, Norcross, Fowler, Follick, & Abrams, 1992). The literature on social support also provi des evidence for the importance of the kind of social support received. For example, George, Blazer, Hughes, and Fowler (1989) explored four dimensions of social support in a longitudinal, multidisciplinary research program geared for older, adult, in-patie nts diagnosed with major depression. Social support was measured on the four dimensional, 30-item self-report D uke Social Support Index. Of the four dimensions, the first th ree reflected objective qualities, number of individuals in support network, quantity of interactions (i.e ., amount of human contact), and practical supports received (i.e., cooking, repairs, sick ca re, transportation, financial). Only the fourth dimension of the instrument reflected subjective perceptions of social support. Results revealed pretreatment seve rity as the most si gnificant predictor of improvement on depression measures, but so cial support was the next most powerful predictor for success. As was shown in Billings and Moos (1985) and Moos (1990), subjective social support indicators predic ted outcome more so than did objective indicators of social support. However, contra ry to the researchers’ expectations, clients were less depressed when they were single a nd had smaller social networks compared to clients who were married and had larger soci al networks. The author s concluded that not all relationships are helpful a nd that perceived satisfaction wi th the relationships is the component most vital to treatment success. Along those lines, Longabaugh, Beattie, Noel, Stout, & Malloy (1993) hypothesized that value of so cial support on treatment outcome depended on a client desire for, or investment in his or her so cial supports. Longabaugh et al. (1993) examined


22 the response of 107 outpatient clients ove r 1 year in a comparative outcome study involving treatment for alcohol addiction. Longabaugh et al. tested their hypothesis by developing an instrument that measured bot h objective and subject ive dimensions of social support. Social Investment was de rived from both objec tive and subjective dimensions of the instrument (e.g., number of people in network and how important the people in the network were to the client), a nd from indicators such as, proportion of time in the client’s life spent with his or her current partner versus time spent living alone. Results of the study indicated that leve l of social support influenced outcome only for clients who were highly invested in thei r social network. The clients reporting high support remained abstinent sign ificantly more days than clients reporting a weaker support system. In contrast, when social suppor t was not valued as hi ghly by clients (i.e., clients reported low social investment), the so cial support variable di d not influence days they remained abstinent. In sum, the evidence supports the impor tance of social support on treatment outcome, especially when measured by the cl ients’ perceived satisfaction, and in some cases, indifference with those supports. Becau se subjective measures of social support have more successfully predicted eventual counseling outcome than have objective measures of social support, this study w ill focus only on subjective social support. Therefore, the social support indicator in this study will not necessarily reflect actual, true social support, but rath er, the client perceptions of that support. Perceived Criticism Within the construct of social support, pe rceived criticism from one’s significant other (i.e., spouse, partner, or parent) has been identified as a critical feature of a client’s general satisfaction with his or her social supports. Per ceived criticism from one’s


23 significant other in particular has been shown to predict treatment outcome for a range of mental health concerns, including schizophr enia (Tompson, Goldstein, Lebell, Mintz, Marder, & Mintz, 1995), posttraumatic stre ss disorder (Chambless & Steketee, 1999), depression (Hooley & Teasdale, 1989), and obsessive-compulsive disorder and panic disorder with agoraphobia (Renshaw, Chambless, & Steketee, 2003). Several studies evaluated criticism from a significant other within the construct of expressed emotion which embodied criticism, hostilit y, and emotional over-involvement. Expressed emotion is assessed by interviews wi th a client’s significant other and in the absence of the client (Okasha, El Akabawi, Snyder, Wilson, Youssef, & El Dawla, 1994; Vaughn & Leff, 1976). Although predictive of treatment outcome, measuring expressed emotion is time-intensive (i.e., requiring a one to two hour semi-s tructured interview, audio recorded, and coded by trai ned raters) and is not from th e perspective of the client themselves (Hooley & Teasdale, 1989). Using a single item that asked cl ients to rate their sense of criticism from significant others, H ooley and Teasdale (1989) monitored relapse rate of 39 major depressed, married clients after successful treatme nt and discharge from three different in-patient hos pitals in England. Factors unde r scrutiny were levels of expressed emotion from the spouse of the clie nt, relationship quality as measured on the Dyadic Adjustment Scale self-report measure (DAS; Spanier, 1976), and client perceived criticism both towards, and from, his or he r spouse. Hooley and Teasdale’s findings demonstrated that expressed emo tion, client’s perceived criticism from his or her spouse, and marital distress measured by the DAS, all were significantly related to client relapse rates. However, the clients’ sense that they were criticized by th eir spouse was the most potent predictor, explaining 38% of the vari ance. Rather than using expressed emotion,


24 Hooley and Teasdale speculated that the si ngle item of perceive d criticism was more incisive, tapping degree of criticism actually “getti ng through to” clients. A similar finding and conclusion was reached a decade later when a client sample of obsessive-compulsive disorder (OCD) or panic disorder with agoraphobia (PDA) treated with behavioral therapy was exam ined (Chambless & Steketee, 1999). The timeintensive measure of expressed emotion was not as predictive of ratings on treatment target goals as was the single item asking clie nts to respond to percei ved criticism. Even when the authors controlled for criticism and hostility from the measure of expressed emotion, perceived criticism remained signi ficant. Chambless and Steketee concluded that objective cri ticism was weakly related to perceive d criticism, and that learning about the latter is important in clinical work. However, there was concern over whether perceived criticism was confounded with a client’s symptom severity or history of the presenting problem. Riso, Klein, Anderson, Ouimette, & Lizardi (1996) evaluated the convergent and discriminant validity of perceived criticism. Riso et al. (1996) studied both the client’s perc eived criticism of his or her significant other and of his or her fa mily/relatives. The study sample consisted of 34 depressed outpatients with a significant ot her at a New York treatment center. Using a number of established measures for compar ison, perceived criticism of significant other showed discriminant validity by low corre lations to symptom severity, history of problem, global functioning, and personality tr aits. In contrast, and as anticipated, perceived criticism showed convergent valid ity with marital adjustment and social functioning. With regard to the measure of c lient perceived criticism from family or relatives, though it showed good discriminant validity, it showed poor convergent


25 validity with subjective soci al support, casting some doubt on the meaning of the measure. Further studies testing the value of perc eived criticism of significant other in predicting treatment outcome were conducted by Renshaw, Chambless, and Steketee (2001, 2003). The researchers studied samples of northeastern outpatients diagnosed with obsessive-compulsive disorder (OCD) or pa nic disorder with agoraphobia (PDA). They tested whether perceived criticism predicted treatment outcome independent of comorbid diagnoses or traits (2001), a nd pretreatment severity usi ng structural equation modeling (2003). In both studies, perceived criticism emerged as a significant, independent predictor of outcome. Few studies using perceived cr iticism have failed to find it predictive of outcome or relapse. One that did was a study of 32 E gyptian outpatients after being treated for depression and evaluated nine months posttre atment (Okasha et al., 1994). However, an important cultural difference in the noted by Ok asha et al. was the high level of client suspicion towards the measure. Clients reportedly questioned the motives for quantifying criticism of their significant others. Seven of the 32 clients refused to respond to the item. The remaining 25 clients only responded to a modified (non-numeric), version that asked whether perceived criticism was either low moderate or high The scale was reduced from a 10-point Likert scale to a 3-point Likert scale. This restricted range reduced power to detect significance and may have accounted for Okasha et al. discrepant findings (Renshaw et al., 2003). The majority of research about perceived cr iticism is that it is an efficient method to gain important prognostic treatment in formation. The research about perceived


26 criticism regarding family members is less clear or established as a predictor of counseling success as perceived criticism of the significant other. Motivation A number of studies comment on the difficu lty of treating a client unmotivated to change (Luborsky, Chandler, Auerbach, Cohen, & Bachrach, 1971; Strupp, Wallach, Wogan, & Jenkins, 1963). Researchers have attempted to characterize nuances of motivational problems in a host of ways, all of which have negatively related to outcome: client unwillingness to engage in the trea tment process (Gomes-Schwartz, 1978), commit to therapy (Gaston, Marmar, Thompson, & Gall agher, 1988), client re sistance to therapy (Bischoff & Tracey, 1995), or reactance, or opposition to therapist influence (Arnow, Manber, Blasey, Klein, Blalock, Markowitz, Rothbaum et al., 2003). Most of these client characteristics have been assessed by traine d raters who coded recorded psychotherapy sessions (Orlinsky, Grawe, & Parks, 1994; Stoolmiller, Duncan, Bank, & Patterson, 1993). Where self-report instruments have been used, the instruments tend to be either lengthy (Jackson, 1984) or limited in concurrent and predictive validity (Seibel & Dowd, 1999). One exception stems from the transthe oretical stages of change model for behavior change (McConnaughy, Prochaska, & Velicer, 1983) that was designed to integrate diverse schools of psychology (Pro chaska, 1999). The stages of change model was largely based on how people change from habit disorders and research has shown support for its ability to detect those ready to benefit from help and those who are not as ready (Clarkin & Levy, 2004). In a study involving volunteers for a sm oking cessation program (DiClemente, Prochaska, Fairhurst, Velicer, Velasquez, & Rossi, 1991), participants were compared on a number of variables depending on whether they were in pr eparation, contemplation, or


27 precontemplation for quitting the habit. These pretreatment categories discriminated well between those prepared to change and those not. For example, participants prepared to change were smoking less cigarettes per day, began smoking later in the day, and had the greatest number of attempts quitting compared to clie nts in contemplation or precontemplation stages. A similar trend c ontinued at six month follow-up: A greater percent of participants in the prepared stag e of change had attempted quitting compared to those in contemplation stage, which in tu rn was greater than t hose in precontemplation. Even more telling in terms of readiness to change was that success in quitting the habit was significantly greater for clients in the preparation stage than clients in either contemplation or precontemplation stages. A study for weight control using hospital staff members explored client factors influencing weight loss (Prochaska et al., 1992). At pretreat ment, Prochaska and colleagues factored in client demographics, previous weight loss history and expectancy for improvement, self-efficacy for weight loss, social supports, process variables related to change (e.g., self-liberation, counterconditioning, etc.), and st ages of change variables. Among these pretreatment variables, a multiple regression analysis revealed stages of change accounted for 6% of posttreatment wei ght loss, comparable to that explained by demographics (7%) and weight loss histor y and expectations for success (9%). Only social support explained s ubstantially more of the outcome variance (17%). More specific to psychotherapy, Brog an, Prochaska, and Prochaska (1999) attempted to distinguish between what cl ients would dropout pr ematurely (against therapist advise before 10 sessi ons), appropriately (agreement with therapist before 10 sessions), or continuance in treatment. Discrimi nant analysis was used to test accuracy of


28 classification into each of these three groups based on either client self-report motivational or demographic variables. Mo tivational, but not demographic variables doubled the accuracy by which clients could be cl assified into premature, appropriate, or continuer therapy groups compared to chance placements. Among variables in the stages of change measure, clients in the precont emplation and contemplation stages improved accuracy of the classification. Expectancy/Hope Client expectation has a relatively long hi story of relating to successful treatment outcomes, and has been summarized in ear ly major psychotherapy outcome reviews (Garfield, 1978; Luborsky et al., 1971). C lient expectancy for outcome has been evaluated a number of times and using differe nt methods. The methods reviewed here are ones drawn from studies that measured client expectancy before client exposure to treatment. The measures thus tap the client s’ preconceived hope about how well therapy will unfold and are not based on perceptions about the counselor or treatment approach. In a relatively early example of the appr oach, Steinmetz et al. (1983) tried to find the best predictors of therapy outcome for 75 depressed adult clients. Steinmetz et al. examined client demographic information, soci al adjustment, perceived locus of control, reading ability, and client expectancy for positive outcome. Before treatment commenced, clients completed the dependent va riable in the study, the Beck Depression Inventory (BDI), and then estimated how mu ch he or she would improve on the same BDI. The greater the difference between self-reported depression and expectancy for change in pretreatment depression indicat ed level of hope. The treatment episode involved eight weeks of psychoeducational grou ps that proceeded according to a specific textbook. By the conclusion of treatment, c lient depression level was significantly


29 reduced. A multiple regression revealed that af ter controlling for pretreatment depression level, positive expectancy accounted for almo st twice as much outcome variance as any other factor (10.5% of outcome variance co mpared to 5.6 % accounted for by the next best predictor-Reading Ability). Using a different method of assessing hope, participants in a weight loss intervention predicted amount of weight they expected to lose prior to treatment (Prochaska et al., 1992). At the end of the weight loss re gimen, the pounds participants expected to lose at pretreatment, along with weight history, explained 9% of the variance in weight loss achieved at posttreatment Another study measured expectations employing a 19-item instrument on pessimism (Hoberman et al., 1988). The aim of the study was to discover qualities predicti ng improvement in a group treatment for depressed clients. Only client impre ssions of group rela tionship strength (e.g., cohesiveness) explained more outcome vari ance than client hope for improvement. Gaston, Marmar, Gallagher, and Thompson (1989) used a variation on client expectancies to improve behavioral therapy, cognitive therapy, or brief dynamic therapy for depression in older adults. Clients were as ked to evaluate their expectancies for the kind of therapeutic tasks that would be helpfu l in relieving their depression. The results showed that only when clients both expected change to occur through behavioral and cognitive tasks, and received cognitive ther apy, did expectancies predict improvement in depression. One conclusion these authors dr ew was that client expectation about how change occurs may have “limited importanc e in predicting outcome in psychotherapy” (pp. 301). General expectation about outcome may be more predictive than tapping expectations about how that outcome will come about.


30 Along these lines, Sotsky and colleagues ( 1991) explored client predictors of positive treatment response in 162 clients completing the full 16-week NIMH TDCRP study. The researchers analyzed 26 client fact ors known to influence outcome, separating them into three major domains: (1) sociode mographic factors; (2) diagnostic and treatment course factors; and (3) symptom, function, and personality factors. Across treatment conditions, only four client factors significantly predicted decrements in depression severity: Two factors were relate d to depression severity (discussed below under Chronicity of Problems). The other tw o factors foretelling success were lower cognitive dysfunction, and germane to this review, higher client expectations of improvement based on a single 5point Likert scale item. Using a similar method of measuring e xpectancy, Chambless, Tran, and Glass (1997) explored predictors of successful treatment with social phobia clients using cognitive-behavioral group therapy. Client variables examined were treatment expectancy, personality pathology (e.g., avoi dant personality disorder, histrionic personality disorder), pretreatment depr ession, and use of medication. Results at termination showed that treatment expectancy was significantly re lated to outcome, but that avoidant traits were mo re powerful. However, at six months follow-up, this was no longer the case. Expectancy and pretest depre ssion each significantly explained 5% of the outcome variance, while the effect of avoi dant traits no longer impacted outcome. The authors recommended that expectancy for positive treatment outcome deserved greater attention in future research. Similarly, Safren, Juster, & Heimberg (1997) studied the relationship between client expectancies about his or her cogn itive-behavioral group treatment for social


31 phobia. Self-report ratings of expectancy for th e 113 adult clients were administered after the first and fourth sessions. Clients also co mpleted self-report instruments assessing their social phobia and depression. Results showed th at after partialling out shared variance between initial expectancies and pretreatme nt severity, expectan cies were negatively correlated ( r = -.21) with both the BDI and Ha milton Rating Scale for Depression. Expectancies were also negatively correlate d (r = -.34) with a So cial Anxiety Scale measured by an independent rater. These resu lts further support the value of considering expectancies as an influence on outcome. Physical Health Physical health status impacts mental we ll-being. Some studies have reported that medical problems can either precipitate a depr essive condition or interfere with recovery from it (Alexopoulos, Barnett, Meyers, Young, Kakuma, Feder, Einhorn et al., 1996). For example, a longitudinal, national sample of participants with musculoskeletal pain revealed that pain was more predictive of depression than other demographic variables analyzed (Magni, Moreschi, Rigatti-Luchin i, & Merskey, 1994). Banks and Kerns (1996) provided an in depth review of literature to explain the high rate of depression accompanying chronic pain. Nine of the 14 re viewed studies that used standardized assessments of depression, such as the Di agnostic and Statistical Manual of Mental Disorders, 4th ed. (DSM-IV; American Psychiatri c Association, 1994) or the Research Diagnostic Criteria (RDC; Spitzer, Endicott, & Robins, 1978), iden tified between 30% and 54% of chronic pain client s also experiencing depression Even in an exception to this trend and Amsterdam researchers did not find major depression related to physical health problems, minor depression (i.e., depr essive symptoms falling short of rigorous


32 diagnostic criteria for major depression) was related to failing physical health (Beekman, Penninx, Deeg, Ormel, Braam, & van Tilburg, 1997). While not as strong as the link to depr ession, Banks and Kerns (1996) reported on studies that showed the high rates of depression with cardi ac disease (17%), coronary artery disease (27%), and stroke (27%). Ot her researchers have doc umented that clients with Parkinson’s disease have a higher incidence of depression. A number of investigators have found a relationship between chronic pain and other psychological problems. Chronic pain ha s shown strong correlations with insomnia (Wilson, Eriksson, D’Eon, Mikail, & Emery, 2002) Gatchel, Polatin, and Kinney (1995) showed that 24% of 310 chronic back pain clie nts also met at least one Axis II disorder. Polatin, Kinney, Gatchel, Lillo, & Mayer ( 1993), using a semi-structured assessment, similarly found that 51% of the sample (n = 2 00) of clients with lower back pain were diagnosed with a personality disorder. A sma ller study of chronic pain clients (n = 17) showed that almost half of the participant also met criteria for Bo rderline Personality Disorder using both a self-re port and semi-structured interview assessment (Sansone, Whitecar, Meier, & Murry, 2001). Chronic pain clients have exhibited d ecreased household chores, outdoor work, family, social, recreational, and work rela ted activities (Kerns & Jacob, 1993; Rudy, Kerns, & Turk, 1988) and Magni et al. (1994) su ggested that disability mediates between chronic pain and the ensuing depressive symptoms. In regard to psychotherapy specifically, medical conditions explained a significant amount of variance in studies exploring ex tratherapeutic pret reatment factors on treatment outcome for depressed clients (Bil lings & Moos, 1985; Krantz & Moos, 1988;


33 Moos, 1990). A year after treatment in a sa mple of depressed older adults, Murphy (1983) showed that physical health problems we re associated with diminished success in treatment outcome. SES and Major Role Satisfaction Socioeconomic status (SES): Multiple reviews have demonstrated a clear relationship between higher e ducation and SES characteristic s and retention in treatment (Garfield, 1994; Petry, Tenne n, & Affleck, 2000). However, the relationship between education and SES is more e quivocal in terms of symptom amelioration (Petry et al., 2000). In one study where SES did play a role, 7% of the outcome variance in the percent of weight loss was explained by SES in c onjoint with age (Prochaska et al., 1992). Although, the effect was small, Diener, Sandvi k, Seidlitz, & Diener (1993) reported a significant correlation (r = .12) between income level and subjective well-being among a United States sample of almost 5000 adults. A review of the extant literature on th e relation between income and subjective well-being within the Unite d States and around the world may help explain its inconsistent impact on symptom improvement in counseling (Diener and Biswas-Diener, 2002). Diener et al. (2002) pos tulated that the affect of income level on well-being depended on whether the indi vidual was living in povert y. Increased income would improve sense of well-being for people livi ng in poverty but would not improve sense of well-being for people not suffering from impoverished living conditions. Theorizing further, Diener, Oishi, and Lucas (2003) s uggested that increased income for people living in poverty might make the differen ce for whether they could afford basic necessities like food and shelter. Attaining basic needs would measurably increase the individual’s sense of wellbeing. In contrast, increased income for people already


34 meeting their basic needs might merely provide for the acqui sition of additional luxuries. Attaining additional luxuries above and beyond basic necessities may result in a diminished return of improving subjective we ll-being. Returning to the purpose of the current investigation, it may be that income level only exerts an effect of counseling outcome for the extreme destitute. When counseling research studies do not include people living in low income levels, SES ma y not predict outcome. Although the aim of the present study is not to test this hypothe sis, SES has influenced outcome in enough studies to merit its inclus ion in the current study. Because time spent at work tends to be gr eater than waking time at home, there is an assumed relationship between life satis faction and job satis faction during waking hours (Tait, Padgett, & Baldwin, 1989). In Ta it and colleagues meta-analysis of 34 studies (n=19,811), they found a significant co rrelation between life satisfaction and job satisfaction (r = .44). A recent example of this relationship was shown in a study involving 474 Korean women. Women reporting th e highest levels of education, income, job satisfaction, and employment stability were four of the six quali ty of life variables that were associated with less depression (Mi-Young, 2001). For people whose primary roles may take place at the home, Greenberger and O’Neil (1993) compared the effect primary role s (at home or at a traditional job) had on anxiety and depression for approximately 300 married participants. Participants were assessed for their commitment to marriage, parenting, and their job. Participants were also assessed for their perceptions of the respective demands each role placed on them, for their evaluation, satisfacti on, and social supports for t hose roles. Results of the investigation showed that men had significan tly less anxiety when they liked and spent


35 more time on job-related tasks. Similarly, women had significantly less anxiety when satisfied with parenting. However, wo men also experienced more anxiety and depression when dissatisfied by their marriage and em ployment. Overall, parental and job satisfaction was the most consistently rela ted variable to anxiety for both men and women, suggesting the importance of respect ive life roles among married couples in counseling treatment. When people are unemployed yet wanting wor k, mental health suffers. In a review of happiness and economic sa tisfaction, Oswald (1997) cite d a number of studies that indicated that unemployed peopl e in particular feel far less content with their lives compared to both wealthy and poor people. Oswald presented results from the first sweep of the 1991 British Household Panel Study studyi ng psychological distress that used a random sample of approximately 6000 particip ants. While no effect of income was found for psychological distress, those without work reported twice the amount of psychological distress as those with work. Consistent with the negative impact of joblessness, Oswald also review ed a number of studies that found a greater risk of suicide attempts among the unemployed versus those employed. For example, Platt & Kreitman (1985) found not only a greater incidence of suicide attempts for those unemployed, but that incidence grew as amount of time jobless increased. More specific to psychotherapy, FirthCozens and Hardy (1992) investigated whether a clinical intervention could improve job attitudes, anxi ety, and psychological functioning in 90 clients usi ng a longitudinal design. After treatment, clients reported feeling more competent, in-con trol, and valued in their job compared to before treatment began. These feelings of job satisfaction were significantly correlated with both a


36 decrease in psychological symptoms and an increase in self-esteem. In a study on extratherapeutic factors for treatment with depressed clients, Billings & Moos (1985) found that job related variables accounted for a significant amount of variance in treatment outcome. Specificall y, a supportive network on the j ob contributed positively to outcome, whereas work stress related negatively to outcome. Employment history of clients has been researched as a prospective factor for retention in substance abuse treatment. Se veral studies found that having job skills or good employment history was related to rete ntion in treatment (Kelly, Blacksin, & Mason, 2001; Platt, 1995) and helped prev ent relapse (Vaillant 1988). Alternatively, some studies have found that substance a buse clients who were either unemployed (Ginexi, Foss, & Scott, 2003), or who had high job counseling needs (McCaul, Svikis, & Moore, 2001), stayed in counseling longer compared to their peers. In summary, the research relating to work or role satisfaction is associated with higher levels of well-being and that when di ssatisfied, mental health is lower. For the present investigation, in order to include homemakers, students, disabled persons, or retired people, a general measure of role satisfaction was chosen over job satisfaction. Because SES can have an impact on psychol ogical distress, a measure of financial security will be included. Complexity/Chronicity of Problems Outcome researchers, Beutler and Harw ood (2000) performed studies to identify client prognosticators of tr eatment outcome. One of the six categories identified was complexity/chronicity of client problems. The category was defined by coexisting Axis I or II disorders, recurrence or frequency of the presenting problem, and the duration that the problem persists. One area of client complexity receiving recent attention is a history


37 of sexual abuse (Clark in & Levy, 2004; Rogers & Norma n, 2004). For example, Laffaye, Kennedy, and Stein (2003) studied 70 female s with a history of physical, sexual, or emotional abuse by an intimate partner. The results revealed that females who had been abused showed lower levels of quality of life on measures of mental health, vitality, and physical, social, and role functioning, compared to females that had not been abused. In another study, the authors attempted to id entify prognosticators of poor response for treatment of 452 in-pa tient Bulimia Nervosa clients (Gleaves & Eberenz, 1993). The researchers formed a high risk category based on clients with a history of multiple outpatient treatment episodes or an in-patient treatment episode, a history of self-injury or suicide attempts, and alcohol or drug abuse. The results showed that 8% of the Bulimia Nervosa clients were at risk for treatment failure and that, a significant number (i.e., 71%), had prior sexual abuse incidents. Nearly a third of those had at least five counts of sexual abuse incidents. Gleaves and Eberen z recommended that hist ory of sexual abuse be evaluated prior to treatment. One study (Safren et al., 1997) documenting the importance of chronicity showed that both greater severity and number of y ears clients had suffered with their presenting symptoms (i.e., social phobias, depression) negatively impacted their hope for achieving a positive treatment outcome. In a study invol ving older adults after treatment for depression, one of the only four significant predictors of 40 examined, was having had incidents of depression three or more times pr ior to the current episode (Bosworth et al., 2002). A small but significant predictor of success in group treatment of depression was the number of previous depressive episode s (Hoberman et al., 1988). Clients suffering from depression tended to fair less well when their current episode had a longer duration


38 compared to clients who had endured th e depression for shorter amounts of time (Alexopoulos et al., 1996; Sotsky et al., 1991) Compared to clients with a single diagnosis of depression, client s diagnosed with double depression (i.e., major depression and dysthymia) predicted a more severe case of depression at the end of treatment in the NIMH TDCRP (Sotsky et al., 1991 ) and greater rate of relaps e after treatment (Keller, Shapiro, & Lavori, & Wolfe, 1982). Less severity of diagnosis and less a hi story of chronic problems have been associated with better outcomes in a psychot herapy outcome reviews (Luborsky et al., 1988). The sum of the evidence supports consid eration of client history of his or her presenting problem when identifying predictors of therapy outcome. Sociodemographic Factors Reviews covering gender, age, and e ducation typically draw conclusions suggesting these variables have little or in consistent influence on success in counseling (Clarkin & Levy, 2004; Garfiel d, 1994). With regard to gender, Clarkin and Levy point that few studies purposely set out to eval uate the impact of gender on outcome. The authors cited three studies that did evalua te gender as a more complex variable concerning the impact of having same or oppos ite genders in client -therapist dyads in counseling. But again the results were mi xed, with one study supporting same gender client-therapist dyads (Fujino, Okazaki & Young, 1994), one study supporting opposite gender client-therapist dyads (Willer & Miller, 1978), and one study supporting neither (Flaskerud & Liu, 1991). The variable age, in an early review of factors impacting outcome (Luborsky et al., 1971) produced inco nsistent findings. Of 11 studies, five had no relationship to outcome, four a positive relationship, and two a negative relationship with outcome. Almost a decade later, a la rge scale meta-analysis between age and


39 counseling success showed a correlation of zero (Smith et al., 1980). Eight years later, in a review about client rela pse after recovering from de pression, age again produced conflicting results (Belsher & Costello, 1988). And in the more recent Clarkin and Levy (2004) review, they concluded that age was not an important factor in determining outcome as a main effect variable. Finally, ed ucation levels show equivocal influence on counseling outcome. Education showed a rela tionship to outcome in 5 of 7 studies reviewed by Luborsky et al. (1971). Some evid ence is presented describing a relationship between lower education and premature dropout rates in treatment (Garfield, 1986; Petry et al., 2000; Wierzbicki & Peka rik, 1993), but was considered weak in affecting outcome (Garfield, 1994). Response Rate As shown by Howard et al. (1986) and late r by Lambert et al. (2001) dose-response curves in psychotherapy show that over half the clients entering treatment have experienced improvements by the eighth sess ion. Research has documented that most clients only attend about six to eight sessions and that so me treatment manuals do not even prescribe the primary interventions un til session six (Beck et al., 1979; Ilardi & Craighead, 1999). In other words, for the aver age client to succeed in therapy, he or she must do so fairly rapidly. While some resear chers have explored client factors for why treatment succeeds or fails (Mohr et al., 1990) others have examined client factors behind response rate t o treatment (Beckham, 1989; Fennell & Teasdale, 1987). Beckham (1989), for example, evaluated pos sible predictors be hind rapid response to psychotherapy of depression with 23 at a mid western training program. Four predictors tested were lear ned resourcefulness, patient co llaboration, therapist empathy, and initial depression level. Clients were cons idered rapid responders if they improved at


40 least 50% from intake to session six. Only initial scores on the Beck Depression Inventory at session one expl ained a significant amount of the treatment variance at termination (55%). Consistent with previous research, the most seve re clients at session one made almost no gains while less severe clients made significantly more improvement by session six. Further, because the greatest improvements were made between intake and session one, Beckham speculated that techni cal interventions may have had less to do with change than factors re lated to the client or to the counseling setting. Studying efficacy of CBT with 34 depressed clients, Fennell and Teasdale (1987) showed that some clients responded more rapidly to treat ment than others and suggested that how clients change might vary as a function of response rate. Some researchers have cited rapid respons e as evidence for nonspecific or common factors mediating clinical improvement (Crits -Christoph et al., 2001; Ilardi & Craighead, 1994). Ilardi and Craighead reviewed eight c ognitive-behavioral th erapy (CBT) studies on depression that had used treatment manua ls. The findings indicated that over 60% of overall symptom amelioration occurred by the fo urth week of treatmen t and that half of the total improvement made by clients occu rred by session six. Moreover, even the treatment gains made by clients classified as “nonresponders,” achieve d their gains in the first four weeks. Contrary to dose-response li terature, no further gains were made for the nonresponders. Because most of the benefits to treatment occurred early, Ilardi and Craighead interpreted this as evidence for nons pecific or, at least “nonspecified,” factors, because the CBT treatment manual does not prescribe cognitive modification techniques until session six.


41 Wilson (1999) proposed that an undetermi ned mechanism, perhaps behavioral home work assignments, may have accounted for the early responses in CBT studies. Wilson suggested that research methods s hould examine client progress on a session-bysession basis to clarify the mechanism of ch ange. Tang and DeRubeis (1999) re-analyzed data from two of the studies reviewed by Ilardi & Craighead ( 1994) and highlighted limitations to statistical analyses averaging ch ange of all clients t ogether. Some clients are especially responsive to therapy, cal led “responders,” while others are less responsive, called “nonresponders.” Although bot h groups displayed rapid change across the first four weeks of treatment, nonresponders underwent no further change after that point whereas responders continued at roughly the same rate of cha nge through to the end of treatment. Unless mediators for the change are investigated on a session-by-session basis (Wilson, 1999) for individual client’s time courses (Tang & DeRubeis, 1999), it will be difficult to explain response rate and id entify clients who will continue to benefit from an approach and those w ho would benefit by a change in approach (Wilson, 1999). To evaluate session-by-session change, re searchers at Brigha m Young University developed a set of procedures using actuarial data generate d from a national data base using the Outcome Questionnaire-45 (OQ-45). Th e OQ-45 is described in detail in the section on instrumentation. The OQ-45 data base (n=11,492) was created in cooperation with a number of agencies across the United States, including community mental health centers, college counseling centers, empl oyee assistance programs, and providers practicing under national managed behavioral health care systems. From this national data base, expected response curves dependi ng on initial OQ-45 score were developed to standardize assessment for ongoing treatment re sponse across client treatment episodes.


42 Client response slopes were generated us ing hierarchical linear modeling (HLM) and were averaged to provide typi cal response patterns depending on initial disturbance level as measured by the OQ-45. In all, 50 different groups of client re sponse were identified by percentile clustering, with at least 220 c lients in each cluster, or 2% of the 11,492 client sample (Finch, Lambert, & Schaalje, 2001). Initial disturbance level was used for thr ee central reasons. First, the first OQ-45 score is the most consistently available c ontinuous measure. Second, research spanning several decades, client problems, and measur es supports the notion that the healthier a client is at the start of treatment, the health ier he or she will be at the conclusion of treatment (Billings & M oos, 1985; Curry, Wagner, & Grothaus, 1990; Gottschalk, Mayerson, & Gottlieb, 1967; Haas, Hill, Lamb ert, & Morrell, 2002; Luborsky et al., 1971; Mann, Jenkins, & Belsey, 1981; Ogles et al., 2002). Third, change scores on the OQ-45 from initial disturbance were highl y predictive of post-treatment change, explaining 17% of the variance after just one session of therapy and as high as 42% of outcome variance after three sessions (Finch et al., 2001). Using these norms, Haas et al. (2002) evalua ted 147 clients at a western university counseling center on whether rapid responders maintained their treatment gains at termination and at follow-up. Response rate to treatment was determined by averaging the difference between the client’s first thr ee sessions to normative ERCs. The categories rapid, moderate, and slow responders correspond ed to the top, middle, or bottom of the distribution for response rate. Ov erall, the results showed that 84% of those classified as rapid responders reliably improved by the e nd of treatment. The other 16% remained


43 about the same as at pretreatment levels. Roughly 90% of slow res ponders either did not change (55%) or grew worse (35%). The results further revealed that client s who had faster rates of response to psychotherapy in the first three sessions had more healthy scores on the outcome measure at both termination and at two years follow-up compared to clients with slower rates of early response. No clients in the fast responder group deteriorated by the end of treatment. Haas and colleagues concluded that early response to therapy predicted eventual outcome. Haas et al. also speculated that faster responders to treatment might have traits that render them more motivated or prepared for counseling. Lambert et al. (2001) and Lambert, Whi pple, Bishop, Vermeersch, Gray, and Finch (2002) both used ERCs to predict clients at high risk of treatment failure early in treatment. The purpose was to alert therapists of the risk so that they could modify approaches and improve outcome. Both studies showed modest reduct ions in failures for these high risk clients, from 85% to 75%. Howe ver, in another study alerting therapists to ERCs of their clientele, Whipple et al. ( 2003) also provided assessments about client’s extratherapeutic characteristics for clients at risk of treatment failure. Early identification and feedback to clinicians improved client out come compared to clients whose clinicians did not have such feedback. The reduction in treatment fa ilures went from 81% down to 51%. The results supported the use of monito ring client progress and using feedback to improve outcome of slow responders to treat ment. The results also suggest that slow responders likely present with mo re severe treatment issues. In summary, regardless of whether manual dr iven studies or client-focused studies were used, the active ingredients determini ng response rate elude researchers. While


44 some researchers speculated that common factors or non-specific factors may have accounted for response rate, others hypothesi zed that homework assignments accounted for early improvements. The approach at Brigham Young provides a method to study session by session change and a procedure of de fining rapid, moderate, or slow response based on normative data. The Brigham Young st udies indicate that rapid and slow responses to treatment maintain themselves over the treatment course and as much as two years later. The researchers ur ge continued studies to examin e client factors contributing to response to treatment (e.g., Haas et al., 2002; Whipple et al., 2003). General Conclusion Research on client social support ove r a number of studies and methods of assessment has been associated with c ounseling outcome. Generally, when clients perceived supports in their networks predict positive tr eatment outcome and that objective indicators of those same networks does not predict treatment response. One of the most important single social support pr edictors of outcome is perceived criticism from significant others. When criticism is str ongly felt by the client, his or her success in counseling is attenuated. Like general measur es of social support, subjective measures are more predictive than objective measures of criticism. Motivat ion has been studied with a variety of methods. One approach that has gained popularity is the transtheoretical stages of change model for behavior change proposed by Prochaska. Research from this approach has yielded modest support for the construct’s prognostic virtues. Related to motivation at the outset of treatment, is clie nt expectancy or hope he or she has for a positive therapy venture. Although pretreatment expectancies have been measured in a number of ways, a simple, general expectancy item measure, has consistently related to treatment success. Client physical health has been linked with mental health well-being.


45 Health conditions, physical pain and chronic pain, have relate d to depressive disorders, insomnia, and personality disorders. When medical health has been included as a predictor of psychotherapy out come, it has explained significant levels of outcome variance. While SES has not consistently pr edicted improvement of client symptoms in treatment, until a demonstrated predictor for th e inconsistent results emerges, it should be included for its potential to explain a dditional outcome variance. Work history, employment, and satisfaction with primary role are variables that have impacted psychological well-being and retention in treat ment. Clients who have chronic or severe episodes of a presenting problem and who ha ve multiple diagnoses tend to show worse prognosis in treatment than clients wi thout such complexity of conditions. Interest in rapid response to treatment has increased to understand mechanisms for the change that occurs in early sessions to improve client response to psychotherapy. Both researchers using manuals and natura listic methods have found rapid response in therapy to be a strong indicator of eventu al treatment outcome. Controversy surrounds the mechanism for its efficacy and recommendations have been made to study change on a session-by-session basis. Recommendations have been made to track response rate on a session-by-session basis to add precision to former analyses on rapid response. In addition, client variables have been posited as a possible explanatory variable accounting for rapid response. Research identifying clie nt factors (i.e., extr atherapeutic and hope factors) might help serve as a springboard for researchers examining response rate. This study will help examine the model of common factors to move this research agenda forward.


46 CHAPTER 3 METHODOLOGY Statement of Purpose The purpose of this study is to evaluate the relationship of client factors and hope/expectancy factors on symptom leve ls for ongoing mental health clients participating in Internet Mental Health Messa ge Boards. Clients’ pe rceptions about their levels of social support and criticism from their significant other, personal motivation and expectancy for a positive treatment experience, satisfaction with their primary role, their health, history of their presenting problem, hi story of past emotiona l or sexual abuse, and sociodemographic factors will be measured. This chapter explicates the research hypotheses, dependent and independent va riables, sample population and sampling procedures, data collection procedures, instrumentation, and method of analysis. Hypotheses The study will test the following null hypotheses: Ho1: There is no significant relati onship between client factors (i.e., extratherapeutic, hope factors) a nd client reported symptom level. Ho2: There is no significant relationship between client reported symptom level and client rated social support. Ho3: There is no significant relationship between client reported symptom level and client rated perceived criticism. Ho4: There is no significant relationship between client reported symptom level and client rated motivation. Ho5: There is no significant relationship between client reported symptom level and client rated hope/expectancy for positive treatment outcome.


47 Ho6: There is no significant relationship between client reported symptom level and client rated satisfaction with his or her primary life role. Ho7: There is no significant relationship between client reported symptom level and client rated physical health. Ho8: There is no significant relationship between client reported symptom level and client rated psycholo gical history of his or her presenting problem. Ho9: There is no significant relationship between client reported symptom level and client rated history of emotional or sexual abuse. Ho10: There is no significant relationship between client reported symptom level and client report of number of counseli ng sessions attended for current presenting problem. Ho11: There is no significant relationship between client reported symptom level and client rated financial security. Ho12: There is no significant relationship between client reported symptom level and client reported education level. Ho13: There is no significant relationship between client reported symptom level and client reported age. Descriptions of Variables Dependent Variables The dependent variable is a global measur e of mental health symptoms based on a client-report composite of three primary areas of their life functioning: amount of symptom distress, quality of their interpersona l relations, and social role at home, work, or school. The composite score provides a general measure of functioning across multiple client diagnoses or mental health conditions. Independent Variables There were 13 independent, or predictor va riables, explored in this study. The first six were based on psychometrically validated instruments: subjective social support, perceived criticism, perception of financ ial security, hope/exp ectancy, health, and


48 motivation. The next seven predictor variable s were single items regarding life role satisfaction, psychological history of the presenting problem, number of counseling sessions attended for the current episode, history of emotiona l or sexual abuse, education level, age, and gender. The literature documents that clients with strong social support s, who do not feel criticized from a significant other, are hope ful about and motivated for treatment, are satisfied with their primary life role, ha ve good health, and relatively short bouts of current symptoms, all have a positive impact on counseling. Particip ants were asked to complete these survey questions from the perspective of how they felt at the time they initiated their current face-to-face counse ling course. Sociodemographic variables for clients have variable impacts on treatmen t course and are included for reasons of statistical control and exploratory reasons. Population The client population was comprised of members of mental health self-help message/bulletin boards who were currently i nvolved in face-to-face professional mental health counseling. The message/bulletin board web sites were self-help forums designed to serve people coping with depression, bi polar disorder, genera lized anxiety, panic disorder, social phobia, sleeping disorders, eati ng disorders, and grief. Forums that were excluded from sampling were conditions of psychosis, autism, mental handicaps, substance abuse or dependence, and antisocial personality disorders. Sampling Procedures Volunteers were solicited by posting messa ges over publicly accessible internet mental health message/bulletin board web sites inviting part icipation in a dissertation research study. Using a similar method in a recent study (Leibert, Archer, Munson, &


49 York, in press), 160 clients were solicited wi thin a 3 months. Therefore, the period of data collection is anticipated to requi re a similar time period of 3 months. Recruitment occurred in two steps. First, searches we re conducted to identify publicly accessible mental health message/b ulleting board forums. Searches primarily targeted prevalent mental health conditions such as depression and anxiety related concerns. Second, rules and re gulations governing research solicitation at potential forums were reviewed because some forums e xplicitly prohibited solicitation for research participants. Other forums, governed by modera tors, reviewed appropriateness of content for the forum they were overseeing. Still ot hers, contained provisi ons that demanded a lengthy review process similar to a research committee and institutional review board (IRB). When no clear guidelines about advertis ing research were st ated, listed contacts were emailed with the request to advertise my study on the respective forum. Third, when either access was granted, or at minimum not prohibited, I completed the registration process (i.e., acquired a unique name and password) for joining the message/bulletin board forum. Fourth, upon becoming a member of the forum, I posted an advertisement asking for participants currently involved in face-to-face professional mental health counseling to volunteer for my study. Interested participants were linked to the following site: http://www.counselingsurve ys.org/do.php?survey=s195197 The site provided the purpose of the study, the estimated time involved, and the consent for participation. Financial remuneration was not offered for pa rticipation and respondents were free to ompletion of the consent allowed access to the survey but the option was given to terminate participation.


50 After completing the survey, participants ag ain had the option to submit or not submit their survey for data analysis. Data Collection Procedures The online survey developed was created using software at CounselingSurveys.org, a free web site maintained for the purpose of constructing Internet surveys. The surveys created at this site are designed to protect the identity of respondents while simultaneously using three methods to help identify possible duplicate responses to preserve the validity of data collection. The three methods i nvolved computer IP (Internet Protocol) addresses, browser cookies, and tim e of survey submission records. The IP address, which identifies a computer or group of computers, is recorded through a process of encryption. If the same computer is used to complete a future survey, the program identifies the duplicat ed, albeit encrypted, IP address. That way, respondent identity is protected, but the survey highlight s that the source of the survey came from a formerly used IP address, and therefore the possibility of a second survey from the same person. Browser cookies, a record of having visi ted a website from a particular computer (e.g., submitted an electronic survey), can rec ognize whether it has al ready been used to submit surveys. Both the IP and cookie me thods provide an indication of whether the same person has sent a survey on more than one occasion. Combining these two methods with time of submission records provides further evidence of a duplicated survey response. This helps rule out the possibility that a different person submitted a survey from a computer from which a survey had been previously submitted. Therefore, the present method protects the anonymity of the respondent while meeting practice standards of validity for web-based surveys ( http://www.counselings urveys.org/counselors/).The responses were stored in the


51 CounselingSurveys.org database and were expor ted as a generic or universal spreadsheet format, comma-separated value (.csv) file. Afte r exporting the data from the site, the .csv file was opened in Microsof t Excel for data analysis. Instrumentation Outcome Questionnaire-45 (OQ-45) The OQ-45 (Lambert, Lunnen, Umpress, Hansen, & Burlingame, 1994) is a brief, 45-item, client self-report measure that is well suited for this study. Generally, it measures a broad spectrum of adult symptoms, syndromes, and stressors (Lambert, Hansen, Umpress, Lunnen, Okiishi, & Bur lingame, 2003). The OQ-45 measures three domains of outcome considered essential in evaluating therapy outcome (Lambert & Hill, 1994): (1) Symptom Distress (e.g., anxiety, de pression) consisting of 25-items; (2) Interpersonal Relations (e.g., subjective degree of dissatisfaction with personal relations) consisting of 11-items; and (3) Social Role (e .g., degree of subjective inability to function at work or school) consisting of 9-items. The OQ-45 has strong psychometric validity and reliability across repeated administrations for both client and non-client populations. The inte rnal consistency for the three subscales was .91 for Symptom Dist ress, .74 for Interpers onal Relations, and .71 for Social Role. However, its overall inte rnal consistency is hi gh (alpha = .93) and a confirmatory factor analysis demonstrated that a one-factor global measure of severity might best represent the instrument (Lam bert et al., 2003). Using a normative population of university students to show stability of th e measure, its test-retest reliability across repeated administrations over 10 weeks wa s .84 overall and from .78 to .82. on its subscales (Lambert et al., 1994; Lambert et al., 2001).


52 The total score of the OQ-45 also shows c oncurrent validity with several scales related to a host of mental health symptoms. It correlate d with The Symptom Checklist 90: SCL-90 (Derogatis, 1983) that measures general psychiatric symptoms, the Beck Depression Inventory (Beck, Ward, Mendels on, Mock, & Erbaugh, 1961), the State-Trait Anxiety Inventory (STAI; Spielberger, Go rsuch, & Lushene, 1970), the Inventory of Interpersonal Problems, (Horowitz, Rosenbe rg, Baer, Ureno, & Villesenor, 1988), and the Social Adjustment Scale (Weissman & Bothwell, 1976) all between .50-.85, and significant at the .01 level (Lambert et al., 2001). In addition to factor analysis, the cons truct validity was also assessed by its sensitivity to change in a University Outpa tient Clinic (Kadera, Lambert, & Andrews, 1996). Results of the study indicated that the total score and s ubscale scores were sensitive to change for clients in ther apy sessions while simultaneously showing no change occurring for the contro l group of students not trying to change. The OQ-45 also demonstrated sensitivity to severity of mental health symptoms. The OQ-45 indicated more severity in an outpatient community me ntal health clients compared to Employee Assistance Program (EAP) clients, which we re significantly more severe compared to non-clients in both the community and at the university (Lambert et al., 2003). The questions, rated on a 5-point Likert scale, have a maximum possible score range from 0 to 180. Higher values indicate lo wer functioning while lower values suggest higher levels of functioning. Extensive norms have established ranges of functioning, from severe to not severe (Lambert et al ., 2003). Norms show that community outpatient adult clients have an average pretreatment OQ score of 77 and that the average person not involved in counseling has an OQ score of 63. A score of 14 points or more reflects


53 reliable change (change not due to measurem ent error and is derived from Jacobson and Truax’s 1991 reliable and clin ical change formulas). The OQ-45 also includes critical items for assessing danger to self, others, as well as substance abuse (Johnson & Shaha, 1996). The capacity of the OQ-45 to measure clinical change acr oss a diversity of symptoms, diagnoses, as well as severity, makes it possible to compare therapist performance (i.e., client outcome) acr oss clients and counseling sites. Duke Social Support Questionnaire (DSSQ) The DSSQ is a 10-item self-report, 5-point Likert scale measure of subjective social support (e.g., feeling listened to, sa tisfied with relationships). Original development of the DSSQ was a 35-item social support measure with 5 subscales: satisfaction with social support, perceived social support, freq uency of social interaction, size of the social network, and instrumental support. Factor analysis using principal factoring supported the 5-factor model, acc ounting for over 80% of the variance. Only items with factor loadings exceeding .40 were retained (Landerman et al., 1989). However, in a follow-up study, only items reflect ing subjectivity of that social support predicted successful treatment recovery (George et al., 1989). For the 10-item subjective social support measure used in this study, in ternal consistency was adequate (Cronbach’s alpha = .79) and the measure is positively related with amelioration of depression symptoms at termination at follow-up (Bos worth et al., 2002). Eight of the 10 Likert responses range from “none of the time” to “all of the time” for questions like, Does it seem that your family and friends (i.e ., people who are important to you) understand you? The remaining two Likert responses range from “extremely dissatisfied” to “satisfied all of the time” for questions like, How satisfied are you with the kinds of


54 relationships you have with your family and friends? Higher scores i ndicate greater perceived social support. The complete in strument is presented in Appendix B. Social Adjustment Scale-Self Report (SAS-SR) The full SAS-SR is 54 six-point Likert self -report scale with nine subscales. The SAS-SR derives from the earlier SAS instrume nt based on an interview format. The SASSR has fair overall internal reliability (Cronbach’s alpha = .7 4). Only the one-item Economic Subscale used in th is study, “Have you had enough money to take care of your own and your family’s financial needs during the last 2 weeks?” Hi gher scores indicate greater impairment of adjustment (Weissman & Bothwell, 1976). The item is presented in Appendix D. Perceived Criticism Measure (PCM) The PCM scale is a single self-report ite m. Clients respond to the question, “How critical is your significant ot her/spouse of you?” The 10-point Likert scale is anchored from 1 (not at all critical) to 10 (very critic al indeed). The scale has been evaluated for psychometric properties in a number of studi es. The results show that the PCM has a Test-retest reliability over 20 weeks of .75 (Hooley & T easdale, 1989), and .80 over two weeks (Renshaw et al., 2003). Although c onvergent validity for PCM is mixed, its discriminant validity is supporte d by nonsignificant correlations with client self-reported anxiety, depression, and personality disorder traits (Renshaw et al.). Predictive validity has been demonstrated by its negative pret reatment relation to eventual treatment outcome (Chambless & Steketee, 1999; Hooley & Teasdale, 1989; Renshaw et al). While construct validity has not b een established, possible confounding variables have been explored. Nonsignificant differences have b een reported between PCM and gender, SES, initial severity, diagnostic gr oup, duration of disorder, or re lative type/significant other


55 (Renshaw et al.). The one item provides an efficient means of gathering clinically relevant information and was more predictive of outcome than a one hour plus measure designed to assess negative expressed emo tion in the client’s family using semistructured interview that re quired trained coders for scoring (Hooley & Teasdale, 1989). The item is presented in Appendix C. University of Rhode Island Change Assessment (URICA Long Form) The URICA is a 32-item, five-point Likert self-report scale measuring stage of change in the four stage model (McConna ughy et al., 1983). Although the highest score on the four subscales (i.e., precontemplation, contemplation, action, and maintenance) identifies the client’s readiness to change, the scale is designed as a continuous measure so that participants can score high on more th an one of the four stages. Construct validity for the instrument has been demonstrated us ing principal component s factor analysis on two samples of adult outpatients for mental health services (McConnaughy et al., 1983; McConnaughy, DiClemente, Prochaska, & Velice r, 1989). Both studies produced four factor solutions correspondi ng to the four proposed subscales in two studies. These results also revealed simple structures for the factor so lutions, therefore supporting the independence of the four factors. Internal reliabilities for each of the four subscales were also adequate, showing Cronbach’s alpha be tween .79 and .88 for precontemplation (P), .84 and .88 for contemplation (C), .84 a nd .89 for action (A), and .82 and .88 for maintenance (M). Examples of items of each subscale are below, arranged in descending order from precontemplation to maintenance: (P) As far as I’m concerned, I don’t have any problems that need changing. (C) I think I might be rea dy for some self-improvement. (A) I am doing something about the problems that had been bothering me.


56 (M) It worries ms that I might slip back on a problem I have already changed, so I am here to seek help. The items are anchored from 1 (Strongly Disagree) to 5 (Strongly Agree). Because this study is designed for clients be ginning counseling for a particular problem, the maintenance subscale has been droppe d. The URICA correlated with treatment outcome with habit problems (e.g., smoking, drinking) and has promise to predict outcome for psychotherapy clients (Prochaska et al., 1992). The scoring algorithm used by some researchers to yield one overall motivation measure is adding contemplation, action, and maintenance subscales and subt racting out precontemplation scores. The higher the client score, the gr eater is his or her level of motivation. This was the scoring algorithm used for the URICA in the present dissertation. The complete instrument is presented in Appendix F. Treatment Expectancy Scale (TES) The TES is a single, 5-point Likert scal e item. Clients res pond to the question, “Which of the following best describes your e xpectations about what is likely to happen as a result of your treatment?” The Likert scal e ranges from 1 (I expect to feel completely better) to 5 (I don’t expect to feel any diffe rent). Because the instrument has only one item, internal reliability data are not av ailable. However, the TES has significantly predicted outcome with depre ssed clients in past studies (es = .22; Meyer, Pilkonis, Krupnick, Egan, Simmens, & Sotsky, 2002) and remained significant even when other client factors were controlle d (Sotsky et al., 1991). An al most identical scale ranging from 1 (I expect to feel much better) to 5 (It’ s possible I could feel a little worse) showed convergent validity with Vanderbilt Psychotherapy Proce ss Scalesubscale “patient hostility” (VPPS) r = -.54, indicating that the lower the expectation for outcome on


57 symptoms of depression, the greater his or her level of hostility (Foley, O’Malley, Rounsaville, Prusoff, & Weissman, 1987). Th e scale provides efficient information potentially predictive of client outcome The item is presented in Appendix E. Health Survey Short-Form-12 (SF-12) The SF-12 is derived from the SF-36, a gene ral health measure containing eight subscales: Physical functioning, Role-Physical Bodily Pain, General Health, Vitality, Social Functioning, Role-Emotional, and Mental Health. In studies of content validity, these eight subscales have been found to be among the most frequently measured health areas (Ware, 1999). Construct validity of the eight subscales has been demonstrated with factor analysis (Ware, Kosinski, & Kelle r, 1994). The measure also provides two composite scores, one for physical health and one for mental health. The physical health component has been shown excellent validity and has an estimated reliability of over .90 (Ware, 1990; Ware et al., 1994). The SF-12 was developed to further shor ten the instrument. The SF-12 correlates about .95 with the longer SF-36 version and ha s a test-retest reliability of .89 over two weeks in the United States (Ware, Kosinski & Keller, 1996). Because the purpose of this instrument is the physical health component, onl y that subscale will be analyzed in this study. All references to the “ SF-12” will hereafter refer on ly to the physical health composite. The SF-12 can be self-administered in a bout two minutes. It has both dichotomous (Yes, No) items and Likert items from three to five response scales. Scoring of the SF-12 is norm-based standardized scores with a mean of 50 and a standard deviation of 10 in the United States (Ware et al., 1996).


58 Single Item Questions Clients responded to seven additional singl e-question items, including gender, age, education, chronicity/counseling history, emo tional and sexual abuse history, number of sessions attended, and primary life role satis faction. The influence of sociodemographic information (i.e., gender, age, education) has been variable (Garfield, 1994), so are included in this study to consider possible influence on outcome. These sociodemographic items are presented in Appendix G. Data Analysis Data will be analyzed using correlation and multiple regression analysis. Zero order correlations will be used to analyze simple relationships between client predictor variables and the dependent variable. Multiple regression analysis will follow to evaluate the relationship of the whole array of client variables on the dependent variable. The adjusted R2, or total variance of the model accounting for measurement error, will provide a measure of how much variance the 13 client factors explained in mental health symptom level. The squared semi-partial correla tions will also be an alyzed to provide a measure of independent contribution to the de pendent variable cont rolling for all other variables in the regression model. Basic a ssumptions of regression will be evaluated and outliers will be examined for undue influence on the results.


59 CHAPTER 4 DATA ANALYSIS The purpose of this study was to evaluate the relationship of client factors and hope/expectancy factors on symptom levels fo r ongoing mental health clients. The study drew from members of public In ternet Mental Health Message Boards that were currently concurrently involved in professional face-to -face mental health counseling. Volunteers were directed to a web page that featured the anonymous client-self-report survey. Completed surveys were distributed to a so ftware database accessible through a password known only to the researcher. Data Collection and Descriptive Statistics One-hundred fifteen mental health message /bulletin board forums were solicited for participation over a period of 13 weeks. Of those 115 forums, 74 (i.e., 64%) permitted the advertisement for this study on the forum message board. Table 1 shows the frequency distribution of forum mental health boards that al lowed members to participate in this study. The majority of participating forums were related to anxiety or depression (i.e., 67.6%); nearly 15% of participating fo rums pertained to general mental health problems or coping with someone with a me ntal health problem and 7% were about mental health counseling treatment. There were 255 responses to the survey. Th e 47 responses that registered either a cookie or duplicate were excl uded from analysis, leaving th e data set with 208 responses. Eight responses were eliminated because either the number of counseling sessions ( n = 5)


60 or gender ( n = 3) was left blank, leaving 200 resp onses. In addition, five outliers were eliminated, leaving a total of 195 responses for the analysis. The sample was primarily Caucasian (i.e ., 96.7 %), and female (i.e., 86.2 %), with 3.3% of the respondents African American, Asian, or Multir acial. The mean age was 36.9 years old ( SD = 10.6), and had at least some coll ege (i.e., 84.3 %) w ith the largest categories some college (i.e ., 36.5 %) or a Graduate Degree (i.e., 23.4 %). The average respondent had coped with his or her presen ting problem “on and o ff my whole life,” and had attended approximately 30-39 counseling sessions. Table 1. Frequency Distri bution of Participating Ment al Health Message Boards Forum Category N Anxiety, Panic, Agoraphobia, Phobia, Stress 22 Depression 12 Problems and Concerns surrounding mental health 11 Bipolar, Mania 5 Obsessive Compulsive Disorder 5 Mental Health Therapy 5 Anxiety and Depression 4 Eating Disorders 4 Sleep Problems 2 PTSD 2 Self-Injury 1 Relationship Problems 1


61 Table 2 shows the descriptive statistics for the continuous variables in the study. The dependent variable, OQ-45 showed similar results ( M = 90.9, SD = 24.9) as found by Lambert and colleagues (2003) published norms for an inpatient sample ( M = 88.8, SD = 26.7). The scores in this sample were not significantly differe nt from the inpatient sample as shown by a 2-sample, independent samples t test, t (195, 207) = .82, p > .05, even though participants in the present study were not currently attendi ng inpatient counseling. The other standardized measure in this st udy, the SF-12, provided norms for the general United States population (Ware, Kosins ki, Turner-Bowker, & Gandek, 2002). The present sample was compared to the norms using a 2-sample, independent samples t test. The results showed that the present sample ( M = 50.1, SD = 12.8) was not significantly different from the norms ( M = 49.63, SD = 9.91), t (195, 6917) = .54, p > .05. Table 2. Descriptive Statistic s for the Continuous Variables Variables N MinimumMaximumMean SD OQ-45* 195 2716890.9 24.9 SSS 195 104528.0 7.8 SAS-SR 195 153.7 1.5 PC* 195 1106.1 2.7 Hope* 195 152.5 0.95 URICA 195 57135106.4 12.5 SF-12 195 17.872.250.1 12.8 Life Role* 194 152.9 1.2 Age 193 126536.9 10.6 Education 192 163.8 1.5 Psy History* 195 264.9 1.0 # Sessions* 186 1104.4 2.7 Lower scores mean greater satisfaction or shorter history of the presenting problem Except for the item Psychological History, all single Likert scale items (i.e., Perceived Criticism, SAS-SR, Hope, Life Ro le Satisfaction, Education, and number of sessions attended) received scor es spanning the entire possible range on the Likert scale.


62 For example, the item Perceived criticism range d from 1 (not at all critical) to 10 (very critical indeed), and responses ranged from one to 10. Decision Rule The number of sessions attended was rec oded because of wide variation of respondent estimates of counseling sessions a ttended (i.e., 1-1500 sessions) or estimating in terms of time in counseling rather th an number of sessions (e.g., on and off for 25 years, in-patient for 1 year). To provid e a consistent respons e code, the amount of counseling was recoded into a 10-point Likert scale as shown in Table 3. The first six items corresponded to increments of 10 sessions The remaining codes were in terms of time in counseling rather than the number of sessions. Because respondents frequently reported attending weekly counseling sessi ons, a number between 50-60 sessions was considered equivalent to a response of 1 year. When the number of sessions reported exceeded 60 sessions, the total number of sessions was divided by 50. Therefore a response of 200 sessions was recoded to fall with in the category of 1-4 years. The mean response ( M = 4.4, SD = 2.7) fell in the category, of 30-39 sessions. Table 3. Recoding Reports of Time or Sessions in Counseling Likert Scale Recode Partitions 1 1-9 sessions 2 10-19 sessions 3 20-29 sessions 4 30-39 sessions 5 40-49 sessions 6 50-60 sessions or 1 year 7 1+ to 4 years 8 5-9 years 9 10-14 years 10 15 years +


63 Reliability Estimates for OQ-45, DSSQ, and URICA Reliability estimates were computed using SPSS 11.0 for Windows Student Version. The Cronbach’s alpha for the OQ-45 full scale score measure of severity ( N = 172) was .94 and comparable to the internal consistency of .93 reported by Lambert and colleagues (1996). The Cronbach’s alpha for the total score for the D uke Social Support Questionnaire (DSSQ) was .90 ( N = 189) and higher than the reliability (Alpha = .79) reported in its validation study (George et al., 1989). Internal reliabilities for each of the f our subscales of the URICA were also comparable to reported levels in past va lidation studies of the instrument (McConnaughy et al., 1983; McConnaughy et al., 1989). The Cr onbach’s alpha for the precontemplation subscale was .85, the contemplation subscal e was .89, the action subscale was .88, and the maintenance subscale was .86. A composite Cronbach’s Alpha was also calculated for contemplation, action, and maintenance subscales because subtracting out precontemplation scores gives the overall motivation score. The composite for the three subscales had a strong internal consistency of .91. In summ ary, all three instruments showed acceptable reliability and were very similar to levels reported in validation studies of the instruments. Test of Assumptions, Multicoll inearity, and Undue Influence Before conducting the test of multiple re gression analysis, underlying statistical assumptions and potential problems were ex amined to ensure the accuracy of the analysis. First, because violations of assumptions for linearity, equal conditional variance, and conditional normality can distort result s (Myers & Well, 1991), these assumptions were tested using SAS version 8.2. Examin ation of the studentized residuals, a


64 standardized measure of error from the regression plane, showed that the three assumptions were met satisfactorily. Another potential problem in multiple regr ession analysis is the presence of multicollinearity, two or more independent va riables related to each other. The presence of multicollinearity reduces power to de tect significance. One measure of multicollinearity used by SAS is whether each independent variable shows high Tolerance (i.e., TOL .1). All independent variables in this study showed a Tolerance exceeding .1 and were therefore were not collinear with one another. The term outlier denotes data points that deviate far from the regression plane and can have a dramatic effect on the analysis (Pedhazur, 1982). Outliers can be detected by evaluating plots of Studentized residuals greater than +/2.0, or for extreme points with respect to the rest of the data. Examinati on of the Studentized re siduals indicated the presence of five outliers (R ange +/2.68 to 3.47). The five outliers were removed from the study. Finally, extreme values on the independent variables can cause undue influence on the regression coefficients and predicte d values. The SAS procedure used provided DFBETAS, a studentized measure of influen ce of each data point on the Intercept and Regression coefficients. The DFBETAS were examined for unusually large values with respect to the rest of the data. Because no large discrepancies were found, the data set appeared free of data exerting undue influe nce on the results. W ith these preliminary analyses completed, discussion is turned towards the findings. Correlation Analysis The Correlation analysis revealed that eight of the 13 client variables were significantly related to lower symptom levels as meas ured by the OQ-45. The correlation


65 matrix is presented in Table 4. The four scales (i.e., DSSQ, SF-12, SAS-SR, and Education Level) where high scores corres ponded to greater soci al support, health, financial security, or education, were all ne gatively related to the OQ-45. The four scales (i.e., Life Role Satisfaction, Psychological History, History of Emotional or Sexual Abuse, Hope/Expectancy) where low scores co rresponded to greater life role satisfaction and hope, shorter histories of the presenting problem and no experiences of abuses, were all positively related to th e OQ-45. The two highest correlations were Life Role Satisfaction and social suppor t (i.e., DSSQ) accounting for 32% and 31% of the variance in the OQ-45 scores, respectively. Chronicit y, or length of time with the presenting problem, was the third highest correlation accounting for 15% of the variance in OQ-45 scores. Health (SF-12), financial secur ity (SAS-SR), education, emotional/sexual victimization all correlated m odestly with the OQ-45. Client variables that were not significantly related to the OQ45 were perceived criticism (i.e., PCM), motivation (i.e., URICA), age, number of sessions attended for presenting problem, and gender. Table 4. Zero-Order Correlations between Client Variable and the OQ-45 OQ-45 DSSQ SAS-SR PCM TES URICA SF-12 OQ-45 1.000 DSSQ -.560*** 1.000 SAS-SR -.292*** .058 1.000 PCM .098 -.191** -.131 1.000 TES .160* -.088 -.043 .139 1.000 URICA .043 -.002 .049 -.079 -.245**1.000 SF-12 -.306*** .162* .329***.013 -.052 .058 1.000 Age -.102 -.058 .068 -.144 .023 .082 -.067 Education -.254** .020 .261** -.143 -.147 .093 .167* Life Role .566*** -.258** -.302** -.019 .135 .014 -.191* Chronicity .382*** -.382***-.112 .011 .119 .052 -.205** # Sessions .019 -.210* .038 .049 -.037 .152* .049 Victim .254** -.325***-.117 .173* .001 .022 -.092 Gender -.062 -.089 .014 .026 -.012 -.073 .047 p < .05. ** P < .01. *** P < .0001.


66 Table 4. Continued. Age Education Life RoleChronicity# Sessions Victim Gender OQ 45 DSSQ SAS-SR PCM TES URICA SF-12 Age 1.000 Education .240** 1.000 Life Role .090 -.109 1.000 Chronicity .053 -.138 .121 1.000 # Sessions .127 .066 -.178* .228** 1.000 Victim .079 -.048 -.004 .333*** .318*** 1.000 Gender -.091 .085 -.241** -.018 .105 .187*1.000 p < .05. ** P < .01. *** P < .0001. Regression Analysis Table 5 shows the results of the regression analysis of the full model of all the client variables on the dependent measure, the OQ-45. The full model is a test of the first hypothesis that there was no si gnificant relationship betw een client factors (i.e., extratherapeutic, hope factors) and client reported symptom level. The results showed that the full model related to the OQ-45, F (13, 163) = 19.70, p <.0001. Therefore, the null hypothesis was rejected and th e alternative hypothesis that client factors predicted decreased levels of client symptoms wa s supported. The model of client variables predicted a substantial 58% of the variance in OQ-45 scores. Table 5. Source Table for the Mode l of Client Variables on the OQ-45 Sources of Variance df Sums of SquaresMean Square F Value Client Model 1363265 4866.551 19.70 Error 16340267247.034 Corrected Total 176103532 Table 6 shows the results of individual client factors independently relating to lower OQ-45 scores controlling for all othe r independent variable s in the model.


67 Table 6. Regression Coefficient Es timates and Independent Variance Client Variable Estimate Standard Error t Value R2 Intercept 104.836 18.526 5.66 DSSQ -1.153 0.185 -6.25*** 0.093 SAS-SR -0.791 0.903 -0.88 0.002 PCM -0.271 0.494 -0.55 0.001 TES 1.564 1.364 1.15 0.003 URICA 0.116 0.080 1.45 0.005 SF-12 -0.211 0.103 -2.06* 0.010 Age -0.379 0.123 -3.08* 0.023 Education Level -1.902 0.908 -2.09* 0.010 Life Role 8.332 1.171 7.11*** 0.121 Chronicity 2.894 1.413 2.05* 0.010 Number Sessions -0.128 0.484 -0.26 0.000 Victimization 5.386 3.151 1.71 0.007 Gender -0.158 3.930 -0.04 0.000 p < .05, ** p < .01, *** p < .0001 Satisfaction with Life Role (Hypothesis 6), Subjective Social Support (Hypothesis 2), older Age (Hypothesis 12), and higher leve ls of education (H ypothesis 9), better Physical Health (Hypothesis 8), and a shorter Psychologica l History (Hypothesis 10) were all significantly associated with lower symptom levels on the OQ-45, controlling for the other client variables. Satisfaction with Life Role explained a significant amount of variance, t = 7.11, p < .0001, r2 = .12, Subjective Social S upport explained nearly the same amount of variance and was significant, t = -6.25, p < .0001, r2 = .09, while Age, t = -3.08, p < .0025, r2 = .02, education, t = -2.09, p < .05, r2 = .01, Physical Health, t = 2.06, p < .05, r2 = .01, and Psychological History, t = 2.05, p < .05, r2 = .01 explained considerably less variance in the OQ-45, though all statisti cally significant. The beta coefficients provide a measure of change in the OQ-45 depending upon the associated client variable. For example, for every one poi nt increase in Life Role Satisfaction there was an associated decrease in the OQ-45 scor e of 8.33 points. Simila rly, every one point


68 change in Subjective Social Support, there was an accompanying change of -1.2 in the OQ-45 score. Nature of the Outliers and Rationale for Outlier Exclusion Four of the five outliers were females, had a mean OQ-45 of 45 ( SD = 4.97), which was about half the mean OQ-45 scor e as the rest of the sample ( M = 90.45, SD = 24.25). The other outlier was a male respondent with an OQ-45 of 129. The five outliers also had low DSSQ scores ( M = 16.6, SD = 6.2) relative to the total sample ( M = 28.14, SD = 7.7). The rest of the client variables were compar able between the outliers and the full sample. The results of a multiple regression analysis conducted on the sample with the outliers included were compared to the results of th e regression analysis of the full sample. A summary of the comparison is presented in Table 7. Table 7. Client model and Individual C lient Factor Semi-Partial Correlations Statistics Outliers Included (N = 200) Outliers Deleted (N = 195) Full Model F Value 13.280*** 19.700*** Adjusted R2 .469 .580 Significant R2 Life Role .10*** .12*** DSSQ .06*** .09*** Victimization .03** Age .03** .02* URICA .01* SF-12 .01* Education .01* Chronicity .01* p < .05, ** p < .01, *** p < .0001 As shown in Table 7, the analysis of the full model with the outliers included was significant at the .0001 level, but explained 11% less variance in OQ-45 scores than did the full model with the outliers deleted. Squa red semi-partial correlations show that the two largest individual pr edictors of symptom levels, Life Role and DSSQ, were the same


69 between the two analyses. Individual client variables explaining less variance differed between the two models. To resolve the di screpancies, a robust regression method was used to analyze the data. A robust analysis is a statistical proce dure that limits the influence of outliers without de leting them from the data set. The robust analysis yields more stable and reliable model estimates (C hen, 2002). The results of the robust analysis were consistent with the regression analysis without the five outliers in this data set. Therefore, the final analysis was based on the data excluding the five outliers. Hypothesis Testing Hypothesis 1 that there was no significant re lationships between client factors (i.e., extratherapeutic, hope factors) and clie nt reported symptom level was rejected. Hypotheses 2-13 were null hypothe ses about each of the 13 i ndividual client variables (e.g., subjective social support, physical health) and fewer symptoms on the OQ-45. Hypothesis 2 that there was no significant relationship between client reported symptom level and client rated social suppor t was rejected when tested by itself (i.e., zero-order correlation) and when all the othe r client variables we re controlled (i.e., squared semi-partial correlation). Hypothesis 3 that there was no significant relationship between client reported symptom level and client rated perceived criticism was not rejected in the zero-order correlation or the squared semi-partial correlation. Hypothesis 4 that there wa s significant relationship between client reported symptom level and client rated motivation was not rejected in either test of relationship (i.e., zero-order correlation or th e squared semi-partial correlation). Hypothesis 5 that there was no significant relationship between client reported symptom level and client rated hope/expect ancy for positive treatment outcome was


70 rejected when tested by itself (i.e., zero-ord er correlation) but not when all the other client variables were controlled. Therefore, hope/expectancy received only limited support and predictive of symptom level only wh en treated with no other client variables as predictors. Hypothesis 6 that there was no significant relationship between client reported symptom level and client rated satisfaction with his or her primary life role was rejected when tested by itself (i.e., zer o-order correlation) and when al l the other client variables were controlled (i.e., square d semi-partial correlation). Hypothesis 7 that there was no significant relationship between client reported symptom level and client rated physical health was rejected when tested by itself (i.e., zero-order correlation) and when all the othe r client variables we re controlled (i.e., squared semi-partial correlation). Hypothesis 8 that there was no significant relationship between client reported symptom level and client rated psychologica l history of his or her presenting problem was rejected when tested by itself (i.e., zer o-order correlation) and when all the other client variables were controlled (i.e ., squared semi-parti al correlation). Hypothesis 9 that there was no significant relationship between client reported symptom level and client rated history of em otional or sexual abuse was rejected when tested by itself (i.e., zero-ord er correlation) but not when all the other client variables were controlled. The history of emotional or sexual abuse was only significant when treated with no other client variables as predictors.


71 Hypothesis 10 that there wa s no significant relationshi p between client reported symptom level and number of sessions atte nded for the presenting problem was not rejected in the zero-order correlation or the squared semi-partial correlation. Hypothesis 11 that there wa s no significant relationshi p between client reported symptom level and financial security was reje cted when tested by itself (i.e., zero-order correlation) but not when all the other clie nt variables were controlled. Therefore, financial support received limited support and was predictive of symptom level only when treated with no other clie nt variables as predictors. Hypothesis 12 that there wa s no significant relationshi p between client reported symptom level and client education level was rejected when tested by itself (i.e., zeroorder correlation) and when a ll the other client variables were controlled (i.e., squared semi-partial correlation). Hypothesis 13 that there wa s no significant relationshi p between client reported symptom level and client age was not rejected when it was the only predictor for symptom level. However, the hypothesis that client age was not predictive of lower symptom levels was rejected when other c lient variables are c ontrolled (i.e., squared semi-partial correlation). Theref ore, age as a predictor of symptom level, was partially supported in this study. Chapter Summary This chapter described the procedures fo r collection of data response rates of participating bulletin/message board websites, decision rules, and statistical analyses. Reliability estimates were provided where re levant and assumptions of the regression analyses were considered. Hypothesis te sting of the 13 research hypotheses were


72 examined. The results supported rejection of research hypotheses 1, 2, 6-9. Partial support was garnered for rese arch hypotheses 5, and 10-12.


73 CHAPTER 5 SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS The purpose of this study was to evaluate the relationship of client factors and hope/expectancy factors on symptom levels for clients undergoing mental health counseling. The following chapter provides a discussion of the findings and suggests clinical implications and future research. Summary of the Study This dissertation study was carried out over public Intern et Mental Health Message Boards. Message Board members who were co ncurrently involved in professional faceto-face mental health counseling were asked to volunteer for participation. One hundred and ninety five message board self-selected volunteers completed the battery of surveys. The survey began with a measure of overall mental health symptoms (i.e., OQ-45) that was the dependent variable in the study. The re st of the survey consisted of measures tapping various aspects of client variables (i.e., extratherape utic and hope factors) that have shown prognostic potential from the counseling and psychotherapy literature. Multiple Regression analysis was used to determine how well the client factor model predicted symptom level of the respondents. The analysis included simple, zero-order correlations between each indivi dual client factor and sy mptom level. The Regression also provided squared semi-partial correla tions, which showed the contribution of explained variance in symptom level of each client factor above and beyond explained when all the rest of the client factor s were already included in the analysis.


74 Conclusions Three overarching research hypotheses were tested and are considered next. The first two research hypotheses will be considered together because they compliment each other. The first research hypothesis was to de termine the extent to which client factors (extratherapeutic and hope) acc ounted for client symptom le vel in an ongoing counseling episode. The second research question was whet her client factors explained over half of the symptom variance as implied by the comm on factors model of treatment outcome? The results revealed that the 13 measured clie nt factors significantl y related to client symptom level and collectively explained over ha lf (i.e., 58%) of the variance in client symptom level. The common factor model theorized by Lambert (1992) proposed that 40% of the variance in outcome was explained by extratherapeutic factors and 15% of the variance in outcome was explained by hope th at treatment would be successful. These two client factors theoretically explained about 55% of the variance in counseling symptom levels. The variance in symptom le vel explained by client variables in the present study (i.e., 58%) very closely appr oximates Lambert’s pr oposed common factor model for client variables (i.e ., 55%). However, in contrast to Lambert’s common factor model, hope was not one of the variables that predicted client symptom level in this study. Overall, the finding supports a common factor model of counseling outcome in that the majority of reported symptom levels were accounted for by factors outside of the counseling room. The third overarching research hypothesis was an exploratory question: Which of the 13 measured client factors were associ ated with positive mental health symptom levels during ongoing therapy treatment? Subjecti ve social support sign ificantly related to client symptom level by itself and when added th e rest of the client factors were already


75 in the regression model. By itself, subjectiv e social support explained the second greatest amount of variance in symptom level (i.e., 31%) of the client factors. When added to the other 12 client variables in the model, subj ective social support ex plained an additional 9% of the variance in symptom level. The finding resembled previous research on subjective social support. For example, higher levels of subjective social support helped prevent relapse in clients with schizophrenia (Koenigs berg & Handley, 1986; Vaughn & Leff, 1976), depression (Brown & Lewins ohn, 1984; Hooley, Orley, & Teasdale, 1986; Krantz & Moos, 1988; Sherbourne, Hays, & Wells, 1995), depressed geriatric clients (Bosworth et al., 2002), and across diagnos es (Spiegel & Wissler, 1986); subjective social support also buffered the stress of j ob loss (Gore, 1978) and he lped in a weight loss program (Prochaska et al., 1992). The amount of variance explai ned by social support when controlled for a number of other factor s has ranged from about 3% (Steinmetz et al., 1983) to 14% (Hobe rman et al., 1988). By way of contrast, in a fe w studies pretreatment soci al support did not predict depression improvement at posttreatment (Billings & Moos, 1985) or at follow-up (Paykel, Cooper, Ramana, & Hayhurst, 1996; Sherrington, Hawton, Fagg, Andrew, & Smith, 2001). Noting the deviation in findings from the literature base, Cooper et al. (1996) observed that their sample was more severely disturbed, or depressed, than in other studies showing the benefits of social supports. Sherrington et al., (2001) joined Paykel et al. in postulating that social support plays less a role in outcome for severely depressed, or symptomatic, clients. Alt hough the present study cannot resolve this discrepancy in findings, a point of measurement is worth noting. Besides the similarity between the two former studies in evaluating a severe population of clients, both used the


76 same measure of social support based on a se mi-structured intervie w procedure (Surtees, 1980). The measure provided information about whom the client lived with, whether he or she had a confiding relationship, the amount and frequency of cont act with others, and attendance at social events. Unfortunately, the measure does not appear to tap the critical variable of subjective or perc eived social support that has contributed to prediction of outcome (George et al., 1989; Landerman, 1989; Riso et al., 1996). It may be that whether social support predicts outcome or not depends more on how (i.e., subjective or objective survey) it is measured than on who (i.e., severely distressed vs. minimally distressed) it is measured on. In general, evidence supporting the influence of the way measurement is executed as more influentia l than content of the measurement is well documented (Hill & Lambert, 2004). Related to subjective social support was the respondents’ perceived criticism from their significant others. Perceived criticism di d not relate to the client reported symptom level in the present study. This null finding is in contrast to previous studies where perceived criticism predicted worse outc ome for clients coping with obsessivecompulsive disorder, panic disorder with agoraphobia, and depressive symptoms (Chambless & Steketee, 1999; Renshaw et al., 2001; 2003). In fact perceived criticism remained predictive of outcome despite controlling for a number of potentially confounding factors, such as hostility and critical comments made by family members during research interviews (Chambless & Steketee, 1999; Hooley & Teasdale, 1989), demographics, duration of the disorder, pretre atment severity, comorbid major depression and Axis II traits (Renshaw et al., 2001) or depression (Ris o et al., 1996). One study that did fail to establish a link between relapse in depression and perceived criticism was a


77 small Egyptian sample (Okasha et al., 1994) but may have occurred due to the cultural differences. However, findings from a study about convergent and di scriminant validity may help explain the present results (Ris o et al., 1996). Perceived criticism was significantly related to marital quality specifically, and not to social relationships in general (e.g., friends, relatives). The measure of perceived criticism may provide a strong subjective indicator of relations with a significant other, but no t with the rest of his or her social network in general. It may be that in the current study, the ro le of the significant other was not as important as general rela tions. Perhaps perceive d criticism is more predictive when study participants are in married relationships. Because data about marital status was not collected in the presen t study, it is not possible to rule out this possibility. In previous studies there wa s not general measure of subjective social support as used in this project and ma y partially account for the lack of significant finding in the present study. However, even when subjectiv e social support is not included in the analysis, perceived criticism wa s not significant. Further, it may be that in the current study, the affect of the significant other wa s not as important as general relations. The client variable motivation, as measur ed with Prochaska and colleagues URICA scale, did not significantly relate to clie nt reported symptom level in this study. One reason for the lack of relationship may have resulted from the crosssectional nature of the design. Neither pretreatment evaluations for motivation nor posttreatment counseling outcomes available to evaluate within s ubject treatment respons e as a function of motivation over time. The self-selected sample suggests that particip ants were motivated already. Motivation may be more appropriate in predicting outcome for shorter-term


78 treatment episodes with a follow-up assessmen t. In addition, the URICA has primarily been used in studies dealing with problems of habit, such as drinking or smoking, not necessarily mental health problems. With these design limitations in mind, the URICA has been criticized for failing to produce va lid stage profiles across study samples (Carey, Purnine, Maisto, & Carey, 1999). Prochaska, DiClemente, Velicer, and Rossi (1993) showed that assessing stage of motivation could improve treatment for programs in smoking cessation, but did not use the URIC A measurement. When using the URICA measurement, Prochaska et al. (1992) showed that three of four subscales on URICA significantly predicted weight loss, but Contemplation stage did not. Then in a study to identify clients who struggle to complete treatment, three of the four subscales were predictive, but Maintenance di d not predict outcome (Broga n, Prochaska, & Prochaska, 1999). When a single measure of URICA was used to measure outcome (Project MATCH Research Group, 1997) one positive re sult emerged at 15-month follow-up, but did was not predictive at othe r points in time nor for all the client groups. In a more recent study of 252 outpatient substance abusers, results showed that the stages of change did not predict outcome in percentage days abstinent (Blanchar d, Morgenstern, Morgan, Labouvie, & Bux, 2003). Only a follow-up regres sion analysis of the four subscales produced a significant predictor: The Action st age. Even so, its effect size was only .02. The authors questioned the clinical utility of the instrument despite its intuitively attractive conceptualization of motivation. Unfortunately, in the present study, it is impossible to determine whether the failure to find significant pr edictive effects of motivation was a result of the design shortcom ings, the more general mental health nature of the sample, or the instrument itself. These considerations await future studies.


79 Client rated hope/expectancy for successful reduction in symptoms received partial support in this study. When no other client va riables were included in the model (i.e., zero-order correlation), the hope measure show ed a small (i.e., 3% of variance) but significant relationship to symptom level. Wh en all the other client variables were included in the model, hope/expectancy no longer explained a si gnificant portion of variance in symptom level. The finding deviat es from several other studies that found a benefit of high hope/expectancy on depressed clients (Hoberman et al., 1988; Sotsky et al., 1991; Steinmetz et al., 1983), socially a nxious clients (Safren et al., 1997) using different measures of hope/expectancy (Cha mbless et al., 1997; Hoberman et al., 1988; Steinmetz et al., 1983), as well as the same measure of hope (Sotsky et al., 1991). These discrepancies might be a function of the retr ospective ratings of hope/expectancies in the present study. Ratings of hope/expectancies may have been influenced by ongoing struggles with the presenting problems. Similar to the pres ent results, a follow-up report from the NIMH TDCRP study showed that the hope/expectancy factor significantly correlated to a composite outcome measur e but when data about the therapeutic relationship was added to the model, hope/expectancy was no longer significant a significant predictor of outcome (Meyer et al., 2002). Although there may be better measures of hope available than the single item used in this study (Borkovec & Nau, 1972; Devilly & Borkovec, 2000), such measures take into account cl ient reactions after meeting the therapist. The goal in the current investigation was to explore what the client presents without influence of the counselor at all. A better evaluation of hope would be to evaluate it prospectively.


80 Client satisfaction with his or her Primary Life Role had a large and significant relationship to client reported symptom level. When no other client factor was included in the regression analysis, satisfaction with primary life role accounted for 32% of the variance in symptom level. Even when all the client factors were already in the model, adding primary life role factor into the mode l explained an addition 12% of the variance in symptom level. When all other client va riables were controlled, Primary Life Role explained the greatest porti on of variance in the present study. In terms of previous research, using a group psychoeducational inte rvention to treat de pression, one client variable examined was satisfaction in 18 areas of life-roles, from health, fitness, and appearance to leisure, housing, religion, and occupati onal status. Life roles did not predict amelioration in depression (Hoberman et al., 1988) in one study, but did in a longitudinal follow-up project about depr ession (Gonzales, Lewinsohn, & Clarke, 1985). Research about work has shown a strong relationship be tween job satisfaction and life satisfaction (r =. 44) in meta-analysis (Tait et al ., 1989). Another study showed that getting reemployed after losing a job promotes qual ity of life, self-esteem, while lowering depression and anxiety compar ed to people that have not returned to employment (Caplan, Vinokur, Price, & Van Ryn, 1989). The Primary Life Role item used in the current study was more focused than the 18item life role used by Hoberman et al. but broader than work role defined by employment status. Whether a pe rson is a student, homemaker, or retiree engaged in voluntary activities, people spe nd a large portion of time in various work or life roles. The pauc ity of research on the affect of life role on psychotherapy outcome is surprising. The si ngle item used in this study, while shows promise as a prognosticator of outcome, l acks psychometric validation and should be


81 interpreted cautiously. Hopefully the present re search will serve as a heuristic to promote the inclusion of this variable in studies desi gned to understand extratherapeutic influences on therapy outcome. Client rated physical health as m easured by the widely used SF-12 was significantly correlated with cl ient reported symptom level wh en other client variables were ignored. By itself, physical health expl ained 9% of the variance in symptom level. When all the client variables were controlle d, physical health explai ned a small (i.e., 1%), but significant portion of a dditional variance. Respondent’s whose physical health was rated more positively was related to lower mental health symptomatology. The finding supports some of the literatur e about the impact of physical health on recovery from mental health problems. For example, physical health conditions predicted relapse in depression at follow-up (Krantz & Moos, 1988; Gonzales et al., 1985) and predicted poorer outcome at treatment completion (M oos, 1990). In another study, fewer chronic medical conditions significantly related to improved depression symptoms in a large national study, though only accounting for 3% of the variance (Sherbourne et al., 1995). In contrast, self-rated phys ical health did not predic t outcome in a population of depressed geriatrics (Boswort h et al., 2002), middle-aged in -patient clients (Billings & Moos, 1985), or a sample whose average age was around 35 years (Hoberman et al., 1988), as was the case in this dissertation. Phys ical health has been evaluated objectively, as on some form of medical problems lis ting, like diabetes, cancer, asthma, etc. (Hoberman et al., 1988; Krantz & Moos, 1988; Sherbourne et al., 1995), or subjective ratings of physical health (Gonzales et al ., 1985), or has been measured using both objective and subjective methods (Bosworth et al., 2002). No clear pattern emerges from


82 these findings except that, when physical health predicts outcome, it tends to be one of the smaller sources of explanatory variance am ong the client variable s. Further research in this area might help to determine how impor tant medical health is in the treatment of mental health. The proxy for Complexity/Chronicity was the clients’ ratings of how long they had dealt with their presenting pr oblem (i.e., Psychological Hist ory). Psychological History did predict symptom level by itself and when all the other client variables were in the model. By itself, Psychological History expl ained a substantial 15% of the variance in symptom level; when all the other client va riables were controlle d, though significant, Psychological History explained on ly an additional 1% of the variance. Although this is a small effect, it appears to be a fairly consiste nt finding in the literature. For example, in a sample involving over 1000 depressed clients, a longer history of prior bouts with depression predicted higher probability of relapse for the curren t episode (Lewinsohn, Zeiss, & Duncan, 1989). The number of previ ous depressive episodes accounted for a small but significant portion of variance (2%) in depressi on outcome (Hoberman et al., 1988) but a very large portion of variance ( 17%) in another study (G onzales et al., 1985). An example of the sometimes variable role of history can be observed in following the work of researchers from North Carolina studying course of depression among geriatric clients. In one study, three or more prior de pressive episodes signi ficantly predicted nonremission (Bosworth et al., 2002) but number of prior episodes did not predict nonremission in an earlier study (George et al., 1989 ). Some studies show that history of previous treatment does not influence outcome in depression (Steinme tz et al., 1983), but many do show a relationship (Cla rkin & Levy, 2004). In sum, the present study replicates


83 a robust finding in the mental health counseling l iterature, that the less incidents of a given problem in one’s life, the better the prognosis. The research question about the relationshi p between client re ported symptom level and client rated history of em otional or sexual abuse was sign ificant when tested by itself (i.e., zero-order correlation). Respondents repo rting a history of abuse had more severe symptom levels, accounting for 6% of symptom level. When th e rest of client variables were controlled, however, the contribution of abuse history to outcome disappeared. In contrast, Gleaves and Eberenz (1993) found that history of sexual abuse was a good prognosticator of poor response to treatment and recommended that history of sexual abuse be evaluated prior to treatment. In a more recent study, people reporting a history of abuse had lower levels of quality of life and mental health compared to people not reporting a history of abuse (Laffaye & Kennedy, 2003). Although not definitively supported as a predictor of lower symptom levels, the variable is worth continued attention in more contro lled research projects. The clients’ Length of Time in Counseli ng, as measured by their estimate of the number of counseling sessions attended, did not relate to symptom le vel. The result was not consistent with the dose-e ffect of psychotherapy literatur e that suggests later gains come increasingly slower (Howard et al., 1986; Lambert et al., 2001). In the present study, regardless of time in treatment, there was no change in symptom reduction. The departure from the dose-effect response is pr obably related to the cross-sectional design and the loose reporting measure of time in treatment. Many respondents provided very gross estimates of number of counseling sessi ons, such as, “about a year” in treatment. It was not possible to know whether some of these reports reflect multiple treatment


84 episodes that were successful, followed by relapse and a return to treatment. Additionally, it was not known wh at kind of treatment clients were receiving, nor how well those treatments were bei ng executed. Alternatively, the failure of the present study to show a dose-response effect might be attrib uted to a different popul ation of clientele. The clients in this study reported severity of symptom levels typically exhibited by clients undergoing inpatient treatment (Lambert et al., 2003). Whether there is a point of severity or complexity of sy mptoms that does not follow th e typical dose-response effect remains to be revealed. There is some evid ence that suggests this may be true. For example, more intense depressive episodes or prior treatment attempts significantly predicted nonremission among a sample of 395 depressed clients (Krantz & Moos, 1988). An earlier onset of depression in one’s life predicted poor response to treatment (Brown & Lewinsohn, 1984). Both the longer durati on of depression episodes reported at pretreatment and the occurrence of minor depression accompanying the major depression, predicted higher severity of de pression posttreatment in the NIMH TDCRP study (Sotsky et al., 1991). In summary, while it was not the purpose of the study to test a dose-response effect, the lack of such findi ng raises the question about whether severity and chronicity of the presenti ng problem alters the phenomenon. As in past research, the demographic vari ables of SES, education, and age in this study were inconsistently related to lower leve ls of symptoms. Client ratings about his or her financial security, the measure of SE S in this study, rece ived partial support individually. When no other cl ient factors were controlle d, financial security accounted for 9% of the variance in symptom level. However, when the full model was already accounted for, financial security did not add significantly to explained variance. Although


85 past studies have demonstrated a fairly consistent relationship between SES and treatment retention (Petry et al., 2000; Wi erzbicki & Pekarik, 1993), the relationship between SES and outcome is not convincing. Several studies, for example, show that income does not influence outcome among c lients with depression (Bosworth et al., 2002; Steinmetz et al., 1983; H oberman et al., 1988). Other re views have suggested there was a modest relationship of education w ith outcome (Luborsky et al., 1988). Client education level was related to client reporte d symptom level in this study, explaining 6% of the variance by itself, and 1% when the rest of the clie nt variables are controlled. Although education level predicted outcome for depresse d clients (Moos, 1990) in one study, it did not predict depression outcome in several others (Bosworth et al., 2002; Brown & Lewinsohn et al., 1984; Hoberman et al., 1988; Rounsaville et al., 1981). Reviews about education status has documente d that while it does influence the duration of treatment (e.g., lower education predicts premature dropout), its affect on symptom outcome has been inconsistent (Garfiel d, 1978; 1994; Wierzbicki & Pekarik, 1993). Perhaps the role of education level exerts a greater influence on symp tom levels with an online population that tends to be a fairly well educated populations, its prognostic value should be regarded tentatively. With regard to age, older client s in the current study provided a unique independent contribution to having lower symptom levels. Although age was not significantly related to lower symp tom levels by itself, when all the client variables were controlled, age significan tly accounted for an additional 2% of the variance. The finding closely resembled one study showing that age significantly accounted for an additional 3.5% of variance in depression outcome with other client variables controlled (Steinmetz et al., 1983). In general, however, age has proved an


86 inconsistent predictor of treatment outcomes (Garfield, 1978; 1994). For example, there are several cases where age was not related to depression outcome (Bosworth et al., 2002; Hoberman et al., 1988; Rounsaville et al ., 1981; Sotsky et al., 1991). In addition, sometimes younger age related to better counseling outcomes (Krantz & Moos, 1988; Steinmetz et al.) but sometimes worse (G onzales et al., 1985). In summary, the demographic variables in this dissertation refl ect the lack of a strong relationship between demographic variables and outcome found in the literature generally. Limitations Weakness in the internal validity resulted from relying on a self-selection sampling process (Ray, 2000). In fact, the evidence is strong that the present sample did not represent the population at large. Participants were white females who were economically well off enough to have access to internet serv ices and were fairly well educated. Many had struggled with his or he r presenting problem or problems over a number of years and may have represented a more complex populat ion than typically f ound in general adult out-patient treatment placements. The particip ants were also not closely controlled on diagnostic features, although the majority of people were drawn from internet self-help sites dealing with either de pression, anxiety, or both. The cross-sectional correlation design made it impossible to draw causal conclusions based on the results. Because th e goal of the study was to evaluate the common factors model of outcome, a major shor tcoming was the design failure to include outcome data. Instead, the evaluation was limited to ongoing progress. There was no way to measure change on the basis of treatment. Therefore, the support found for the common factors model in this project must be considered tentative. To assess outcome, it


87 would be better to conduct a randomized, repe ated measures, pretes t-posttest longitudinal design to analyze pretreatment client factors on actual amounts of change. A related limitation was that client fact ors may have been confounded with present symptom levels. Even though clients were as ked to rate themselves on the variables before treatment began, some clients reported being in treatment for a number of years. The validity about the hope/e xpectancy and motivation measure would therefore be questionable. While it has been suggested th at social support may be contaminated by depression (Henderson, 1984), the measure of so cial support in this study was shown to be independent of depression in a longitudinal analysis (B lazer & Hughes, 1991). Also, in the present study, hope/expectancy and motiva tion levels were similarly confounded by symptom levels as were social support le vels. However, hope/expectancy and motivation levels did not correlate with varying sympto m levels as subjective social support did. Implications and Recommendations Research The primary finding of this research is th at client factors as postulated in the common factors model of outcome were highly related to levels of mental health symptomatology. One implication is that, if clie nt factors relate to lower symptom levels among clients receiving ongoing counseling, then c lient factors might also relate to lower symptom levels at outcome. If client factors relate to lower symptom levels at outcome, then these client factors measured at pret reatment might predict ultimate outcome. In other words, just as client factors highly re lated to symptom levels in this study, client factors might similarly exert a large influence over the success or failure on treatment in general.


88 Over the last few decades, researcher s have tried to determine unexplained variation in treatment outcomes (Bosworth et al., 2002; Mohr et al ., 1990), distinguish between clients who improve, stagnate, or de teriorate in treatment (Steinmetz et al., 1983), identify factors associated with th e high risk and prevalence of recurrent depression episodes (Belsher & Costello, 1988 ), identify client factors behind response rate to treatment (Beckham, 1989; Fennell & Teasdale, 1987), and intervene according specific identified risk factor s to improve outcome (Gonzales et al., 1985; Haas et al., 2002; Krantz & Moos, 1988; Rounsaville, et al., 1981; Rude & Rehm, 1991; Sotsky et al., 1991; Whipple et al., 2003). The client f actors of social suppor t, satisfaction with primary life role (e.g., student, home maker), prior attempts coping with the presenting problem, and to a lesser extent, education leve l, health satisfaction, and complexity of symptoms (i.e., history of victimization) might help future researchers identify an important source of variance in response rate. Whether a study method follows a naturalistic or RCT approach, measuring client symptom change session by session provides more precise information about what causes rate of response (Tang & DeRubeis, 1999; Wilson, 1999). Using that approach (Haas et al., 2002), results have indicated that what ever causes rapid and slow responses to treatment, that those outcomes persist for as much as two years posttreatment. The influential extratherapeutic factors identified in the current study might be used in a longitudinal study to explain response rate to treatment. Knowledge about client symptom level after just the first three sessi ons has been shown to improve treatment for at risk clients (Haas et al.). Adding extrat herapeutic prognosticators of outcome into the picture may advance the cause of determining, “ What specific treatment, by whom is


89 most effective for this individual with that specific problem, and under which set of circumstances” (Paul, 1967, p. 111)? Clinical For adult, white, well-educated females, satisfaction with their social supports, primary life role (e.g., student, retiree), fe wer prior attempts at coping with their presenting problem, and to a lesser extent, highe r education level, satisfaction with their physical health, and having less comorbidity of symptoms (i.e., history of victimization), all related to lower mental health symptoms. Knowledge in these areas at treatment intake might help shape the clinician create goals with the client to improve chances for success. Perceived dissatisfaction with current social supports and/or primary life roles may have the greatest relationship to clients’ ultimate well-being. A ddressing such dissatisfaction might help promote recovery. There may be a ne ed to incorporate social skills training or coping skills to promote greater socializa tion practices to expand the client support network. Low education or job satisfacti on might suggest a strong need for career assessment and counseling. A long history of the presenting problem (e.g., social anxiety), comorbid problems (e.g., emotional abuse), may also indicate the need for a longer term treatment approach. Brief thera py may be advised for clients with strong resources but contraindicated for clients who do not. The large portion of variance explained by client variables gives credence to the therapeutic approaches that build on client strengths and help prom ote coping skills in deficit areas. Client changes that have occu rred between sessions, starting with changes that may have occurred between the intake appointment and the first counseling session can help mobilize client resources (Hubble, Duncan, & Miller, 1999c). As these authors point out, common factors theory of change does not transl ate into a counseling approach

PAGE 100

90 devoid of technique. Assisting the client recognize life-impr oving actions executed on his or her own can be noticed and amplified by counselors (Hubble et al ., 1999c). Clinician knowledge of pretreatment client factors may potentially help improve service and at the same time, teach therapists what factors dict ate the most effective interventions. Future research taking advantage of client feedback about resources at in take and his or her symptoms throughout the counseling process ho lds promising benefits to providers and clients alike.

PAGE 101

91 APPENDIX A ADVERTISEMENT AND CONSENT I would appreciate your help with my survey. Hi, I am a graduate researcher at the Univ ersity of Florida studying people currently receiving professional face-to-face mental health counseling. Specifically, I am examining how things in our lives (e.g., fr iends, family support, health, etc.) affect symptoms while in counseling. If you are willing to participate, please visit this web site: http://www.counselingsurveys.org/do.php?survey=s195197 The survey here takes between 20-30 minut es to complete. I am will to send an electronic copy of my results upon request after the study is co mpleted. This research has been approved by the University of Florida Institutional Review Board. Thank you for your time and attention, Todd Leibert Informed Consent The purpose of this dissertation research is to better understand how things in your life (e.g., social/family support, health, employme nt) affect counseling, and ultimately, to help improve the quality of professional mental health counseling servic es. I invite you to participate in this study and am requesting you r consent to take part in this research. Participation requires you to complete an a ssessment that will ta ke approximately 20-30 minutes. Before you agree to take part in this study, please be aware of the following: 1. You must currently be involved in face-to-face professional counseling.

PAGE 102

92 2. Your participation in this study is completely voluntary an d there are no penalties for not participating. You also ha ve the right to stop particip ating at any time without penalty. 3. There are no known physical, psychological, or economic risks associated with participation in this study. 4. There is no compensation to you for partic ipating in this study. There are no direct benefits to you for part icipating in this study. 5. There is a large range in the questions as ked, from gender and education level to more sensitive questions about abuse history and sexual fulfillment (Examples of the kinds of items on this survey ask you to rate yourse lf on a number of items: “I get along with others,” “ How satisfied are you with the kinds of relationships you have with your family and friends?” and “I wish I had more ideas on how to solve the problem.”) 6. No names are asked for in this study. I ndividual results of the study will remain confidential. All data collect ed will remain confidential w ithin the bounds of internet usage. 7. Privacy Policy and Security Notice: This web site does not co llect any electronic information in a manner that could be used to identify who you are. Th is site does not use encryption technologies, theref ore any information you provide could be observed by a third party while in transit. 8. You have the right to ask any additional questions of the res earcher concerning the purpose of the study, your rights as a particip ant, and how the information will be used. To continue, click the big button at the bottom of this screen. I have read the above document and agree to participate. Whom to contact if you have questions about the study: Todd Leibert, Doctoral Candidate, Department of Counselor Educati on, University of Florida, P.O. Box 117046, 1215 Norman Hall, Gainesville, FL 32611-7046, (352) 392-0731, leibert@ufl.edu Supervisor Contact : James Archer, Jr., Department of Counselor Education, 1215 Norman Hall, Gainesville, FL 32611-7046, (352) 392-0731, ext 231, jarcher@coe.ufl.edu Whom to contact about your rights as a research participant in the study: UFIRB Office, Box 112250, University of Fl orida, Gainesville, FL 32611-2250; phone number: (352) 392-0433.

PAGE 103

93 APPENDIX B DUKES SOCIAL SUPPORT QUESTIONNAIRE 1) Does it seem that your family and frie nds (i.e., people who are important to you) understand you? a) none of the time b) hardly ever c) some of the time d) most of the time e) all of the time 2) Do you feel useful to your family a nd friends (i.e., people important to you)? a) none of the time b) hardly ever c) some of the time d) most of the time e) all of the time 3) Do you know what is going on with your family and friends? a) none of the time b) hardly ever c) some of the time d) most of the time e) all of the time 4) When you are talking with your family a nd friends, do you feel you are being listened to? a) none of the time b) hardly ever c) some of the time d) most of the time e) all of the time 5) Do you feel that you have a definite ro le in your family and amount your friends? a) none of the time b) hardly ever c) some of the time d) most of the time e) all of the time 6) Can you talk about your deepest problems with at least some of your family and friends?

PAGE 104

94 a) none of the time b) hardly ever c) some of the time d) most of the time e) all of the time 7) In time of trouble, can you count on at least some of your family and friends? a) none of the time b) hardly ever c) some of the time d) most of the time e) all of the time 8) When you are with you family and friends, how often do you feel lonely? a) none of the time b) hardly ever c) some of the time d) most of the time e) all of the time 9) How satisfied are you with the kinds of re lationships you have with your family and friends? a) extremely dissatisfied b) very dissatisfied c) somewhat dissatisfied d) satisfied most of the time e) satisfied all of the time 10) Are you satisfied with how often you see your friends and relativ es; that is, do you see them as often as you want to? a) extremely dissatisfied b) very dissatisfied c) somewhat dissatisfied d) satisfied most of the time e) satisfied all of the time

PAGE 105

95 APPENDIX C PERCEIVED CRITICISM SCALE (Hooley & Teasdale, 1989) Directions: On a scale from `Not at all Critical` to `Very Critical,` Please check the number on the scale that best describes you. 1. How critical do you think the most signif icant person in your life is of you? Not at all Critical 1 2 3 4 5 6 7 8 9 10 Very Critical

PAGE 106

96 APPENDIX D SOCIAL ADJUSTMENT SCALE-SELF REPORT (SAS-SR) Weissman & Paykel 1. Have you had enough money to take care of your own and your family’s financial needs during the last 2 weeks? a) I had enough money for needs. b) I usually had enough money, with minor problems. c) About half the time I did not have enough money but did not have to borrow money. d) I usually did not have enough mo ney and had to borrow from others. e) I had great financial difficulty

PAGE 107

97 APPENDIX E TREATMENT EXPECTANCY SCALE Which of the following best described your ex pectations about what was likely to happen as a result of your treatment? 1. I expected to feel completely better. 2. I expected to feel somewhat better. 3. I wasn’t sure what to expect. 4. I didn’t expect to feel much difference. 5. I didn’t expect to feel any different.

PAGE 108

98 APPENDIX F URICA (LONG FORM) UNIVERSITY OF RHODE ISLAND CHANGE ASSESSMENT This questionnaire is to help us improve services. Ea ch statement describes how a person might feel when starting therapy or approaching problems in their lives Please indicate the extent to which you tend to agree or disagree with each statement. In each case, ma ke your choice in terms of how you feel right now, not what you have felt in the past or would like to feel. For all the statements that refer to your "problem", answer in terms of what you write on the "PROBLEM line below. And "here" refers to the place of treatment or the program. There are FIVE possible responses to each of the items in the questionnaire: 1 = Strongly Disagree 2 = Disagree 3 = Undecided 4 = Agree 5 = Strongly Agree 1. As far as I'm concerned, I don't have any problems that need changing. 2. I think I might be ready for some self-improvement. 3. I am doing something about the problems that had been bothering me. 4. It might be worthwhile to work on my problem. 5. I'm not the problem one. It doesn't make much sense for me to be here. 6. It worries me that I might slip back on a problem I have already changed, so I am here to seek help. 7. I am finally doing some work on my problem. 8. I've been thinking that I might want to change something about myself. 9. I have been successful in working on my problem but I'm not sure I can keep up the effort on my own. 10. At times my problem is difficult, but I'm working on it. 11. Being here is pretty much a waste of time for me because the problem doesn't have to do with me. 12. I'm hoping this place will help me to better understand myself. 13. I guess I have faults, but there's nothing that I really need to change. 14. I am really working hard to change. 15. I have a problem and I really think I should work at it. 16. I'm not following through with what I had already changed as well as I had hoped, and I'm here

PAGE 109

99 to prevent a relapse of the problem. 17. Even though I'm not always successful in cha nging, I am at least working on my problem. 18. I thought once I had resolved my problem I would be free of it, but sometimes I still find myself struggling with it. 19. I wish I had more ideas on how to solve the problem. 20. I have started working on my problems but I would like help. 21. Maybe this place will be able to help me. 22. I may need a boost right now to help me maintain the changes I've already made. 23. I may be part of the problem, but I don't really think I am. 24. I hope that someone here will have some good advice for me. 25. Anyone can talk about changing; I'm actually doing something about it. 26. All this talk about psychology is boring. Why can't people just forget about their problems? 27. I'm here to prevent myself from having a relapse of my problem. 28. It is frustrating, but I feel I might be having a recurrence of a problem I thought I had resolved. 29. I have worries but so does the next guy. Why spend time thinking about them? 30. I am actively working on my problem. 31. I would rather cope with my faults than try to change them. 32. After all I had done to try to change my problem, every now and again it comes back to haunt me. Scoring Precontemplation items 1, 5, 11, 13, 23, 26, 29, 31 Contemplation items 2, 4, 8, 12, 15, 19, 21, 24 Action items 3, 7, 10, 14, 17, 20, 25, 30 Maintenance items 6, 9, 16, 18, 22, 27, 28, 32

PAGE 110

100 APPENDIX G CLIENT SOCIODEMOGRAPHIC INFORMATION (9-ITEMS) 1) Gender : Male: _____ Female: _____ 2) Age : _____ 3) Racial/Ethnic Background: African Descent/Black ____ American Indian ____ Asian ____ Caucasian/White ____ Hispanic/Latino(a) ____ Multiracial ____ 4) Education : Some High School _____ High School _____ Some College _____ Undergraduate Degree _____ Some Graduate School _____ Graduate Degree _____ 5) Hours employed per week: _____ 6) Length of Employment : Estimate the amount of time at the longest held job you have had: 1. Less than 6 months. 2. Between 6 months and 1 year. 3. 1-2 years. 4. 2-5 years. 5. 5-10 years. 6. Other. 7) Primary Life Role: I am satisfied with or am making progress to ward a rewarding career/primary life role (e.g., retirement, motherhood, career, school, etc.). 1. Very Satisfied 2. Satisfied

PAGE 111

101 3. Unsure 4. Dissatisfied 5. Very Unsatisfied 8) Previous Psychological History : Please estimate how long you have experien ced significant psychological (e.g., anxiety, depression) problems during your life? 1. This last month only. 2. About 2-6 months. 3. About 6 months to 1 year. 4. The last few years. 4. For on and off my whole life. 5. For as long as I can recall. 9) Please check if you h ave ever been a victim of : Rape ____ Incest ____ Sexual molestation as a child ____ Physical/emotional a buse as a child ____ Physical/emotional/sexua l abuse by partner ____ N/A ____

PAGE 112

102 REFERENCE LIST Addis, M. E., Wade, W. A., & Hatgis, C. (1999). Barriers to dissemination of evidencebased practices: Addressing practitio ners’ concerns about manual-based psychotherapies. Clinical Psychology: Science and Practice 6, 430-441. Alexopoulos, G.S., & Meyers, B.S., Young, R.C. Kakuma, T., Feder, M., Einhorn, A., & Rosendahl, E. (1996). Recovery in geriatric depression. Archives of General Psychiatry 53, 305-312. American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders (4th ed.). Washington, DC: Author. Arnow, B.A., Manber, R., Blasey, C., Klei n, D.N., Blalock, J.A., Markowitz, J.C., Rothbaum, B.O., Rush, J.A., Thase, M.E ., Riso, L.P., Vivian, D., McCullough, J.P., Keller, M.B. (2003). Therapeutic reactan ce as a predictor of outcome in the treatment of chronic depression. Journal of Consulting and Clinical Psychology, 71, 1025-1035. Auerbach, A.H., Luborsky, L., & Johnson, M. (1 972). Clinicians’ predictions of outcome of psychotherapy: A trial of a prognostic index. American Journal of Psychiatry 128, 830-835. Banks, S.M., & Kerns, R.D. (1996). Explaining high rates of depression in chronic pain: A diathesis-stress framework. Psychological Bulletin, 119, 95-110. Barlow, D.H. (1994). Psychological interven tion in the era of managed competition. Clinical Psychology: Science and Practice 1, 109-122. Beck, A.T., Rush, A.J., Shaw B.T.F., & Emery, G. (1979). Cognitive therapy of depression New York: Guildford Press. Beck, A.T., Ward, C.H., Mendelson, M., Moc k, J., & Erbaugh, J. (1961). An inventory for measuring depression. Archives of General Psychology 4, 53-63. Beckham, E.E. (1989). Improvement after ev aluation in psychotherapy of depression: Evidence of a placebo effect? Journal of Clinical Psychology, 45, 945-950. Beekman, A.T.F., Penninx, B.W.J.H., Deeg, D.J.H., Ormel, J., Braam, A.W., & van Tilburg, W. (1997). Depression and physical health in later life: Results from the Longitudinal Aging Study Amsterdam (LASA). Journal of Affective Disorders, 46, 219-231.

PAGE 113

103 Belsher, G., & Costello, C.G. (1988). Relapse after recovery from unipolar depression: A critical review. Psychological Bulletin, 104, 84-96. Bergin, A.E., & Garfield, S. L. (1994). Handbook of psychotherapy and behavior change (4th edition). New York: Wiley. Bergin, A.E., & Lambert, M.J. (1978). The ev aluation of psychotherapeutic outcomes. In S.L. Garfield, & A.E. Bergin (Eds.), In Handbook of psychotherapy and behavior change: An empirical analysis (2nd ed., pp. 139-190). Wiley: New York. Beutler, L.E. (2002). The dodo bird is extinct. Clinical Psychology: Science and Practice, 9(1), 30-34. Beutler, L.E., & Clarkin, J.F. (1990). Systematic treatment selection: Toward targeted therapeutic interventions New York: Brunner/Mazel. Beutler, L.E., Clarkin, J.F., & Bongar, B. (2000). Guidelines for the systematic treatment of the depressed patient. New York: Oxford University. Beutler, L.E., & Harwood, T.M. (2000). Prescriptive psychotherapy : A practical guide to systematic treatment selection New York: Oxford University Billings, A.G., & Moos, R.H. (1985). Life stressors and social resources affect posttreatment outcomes among depressed patients. Journal of Abnormal Psychology, 94, 140-153. Bischoff, M.M., & Tracey, J.G. (1995). Clie nt resistance as predicted by therapist behavior: A study of sequential dependence. Journal of Counseling Psychology, 42, 487-495. Blanchard, K.A., Morgenstern, J., Morga n, T.J., Labouvie, E., & Bux, D.A. (2003). Motivational subtypes and continuous measures of readiness for change: Concurrent and predictive validity. Psychology of Addictive Behaviors, 17, 56-65. Blazer, D.G., & Hughes, D.C. (1991). Subj ective social support and depressive symptoms in major depression: Sepa rate phenomena or epiphenomena. Journal of Psychiatric Research, 25, 191-203. Borkovec, T.D., & Nau, S.D. (1972). Credib ility of analogue therapy rationales. Journal of Behaviour Therapy and Experimental Psychiatry, 3, 257-260. Bosworth, H.B., Hays, J.C., George, L.K., & Steffens, D.C. (2002). Psychosocial and clinical predictors of unipolar depression outcome in older adults. International Journal of Geriatric Psychiatry 17, 238-246. Brogan, M.M., Prochaska, J.O., Prochaska, J.M. (1999). Predicting termination and continuation status in psychotherapy using the transtheoretical model. Psychotherapy, 36, 105-113.

PAGE 114

104 Brown, J., Dreis, S., & Nace, D. K. (1999). What really makes a difference in psychotherapy outcome? Why does managed care want to know? In M. A. Hubble, B. L. Duncan, & S. D. Miller (Eds.), The heart and soul of change: What works in therapy (pp. 389-406). Washington, DC: Am erican Psychological Association. Brown, R.A., & Lewinsohn, P.M. (1984). A psyc hoeducational approach to the treatment of depression: Comparison of group, indivi dual, and minimal contact procedures. Journal of Consulting and Clinical Psychology, 52, 774-783. Caplan, R.D., Vinokur, A.D., Price, R.H., & Van Ryn, M. (1989). Job seeking, reemployment and mental health: A random ized field experiment in coping with job loss. Journal of Applied Psychology, 74, 759-769. Carey, K.B., Purnine, D.M., Maisto, S.A., & Carey, M.P. (1999). Assessing readiness to change substance abuse: A crit ical review of instruments. Clinical Psychology: Science and Practice, 6, 245-266. Chambless, D.L. (2002). Beware the dodo bi rd: The dangers of overgeneralization. Clinical Psychology: Science and Practice, 9, 13-16. Chambless, D.L., & Ollendick, T.H. (2001) Empirically supported psychological interventions: Controversies and evidence. Annual Review of Psychology 52, 685716. Chambless, D.L., & Steketee, G. (1999). Expressed emotion and behavior therapy outcome: A prospective study with ob sessive-compulsive and agoraphobic outpatients. Journal of Consulting and Clinical Psychology 67, 658-665. Chambless, D.L., Tran, G.Q., & Glass, C.R. (1997). Predictors of response to cognitivebehavioral group therapy for social phobia. Journal of Anxiety Disorders, 11, 221240. Chen, C. (2002). Robust regression and ou tlier detection with the ROBUSTREG procedure. Proceedings of the Twenty-Seventh Annual SAS Users Group International Conference. Cary, NC: SAS Institute. Chiesa, M., Drahorad, C., & Longo, S. (2000) Early termination of treatment in personality disorder treated in a psychotherapy hospital: Quantitative and qualitative study. British Journal of Psychiatry, 177, 107-111. Clarkin, J.F., & Levy, K.N. (2004). The influenc e of client variables on psychotherapy. In M.J., Lambert (Eds.), Bergin & Garfield’s Handb ook of psychotherapy and behavior change (5th ed., pp.194-226). New York: John Wiley & Sons. Crits-Christoph, P., Connolly, M.B., Gallop, R., Barber, J.P., Tu, X., Gladis, M., & Siqueland, L. (2001). Early improvement during manual-guided cognitive and dynamic psychotherapies predicts 16-week remission status. Journal of psychotherapy: Practice and Research, 10, 145-154.

PAGE 115

105 Curry, S., Wagner, E.H., & Grothaus, L.C. ( 1990). Intrinsic and extr insic motivation for smoking cessation. Journal of Consulting and Clinical Psychology 58, 310-316. Devilly, G.J., & Borkovec, T.D. ( 2000). Psychometric properties of the credibility/expectancy questionnaire. Journal of Behavior Therapy and Experimental Psychiatry, 31, 73-86. DiClemente, C.C., Prochaska, J.O., Fairhurst S.K., Velicer, W.F., Velasquez, M.M., & Rossi, J.S. (1991). The process of smoking cessation: An analysis of precontemplation, contemplation, and preparation stages of change. Journal of Consulting and Clinical Psychology, 59, 295-304. Diener, E., & Biswas-Diener, R. (2002). Will money increase subjective well-being? Social Indicators Research 57, 119-169. Diener, E., Oishi, S., Lucas, R.E. (2003). Pers onality, culture, and subjective well-being: Emotional and cognitive evaluations of life. Annual Review of Psychology, 54, 403425. Diener, E., Sandvik, E., Seidlitz, L. & Di ener, M. (1993). The relationship between income and subjective well-being: Relative or absolute? Social Indicators Research 28, 195-223. Division 12 Task Force. (1995) Training in and disseminatio n of empirically-validated psychological treatments: Re port and recommendations. The Clinical Psychologist 48, 3-23. Elkin, I., Shea, T., Watkins, J.T., Imber, S.D., Sotsky, S.M., Collins, J.F., Glass, D.R., Pilkonis, P.A, Leber, W.R., Docherty, J.P ., Fiester, S.J., & Parloff, M.B. (1989). National Institute of Mental Health treatme nt of depression collaborative research program: General effectiveness of treatments. Archives of General Psychiatry, 46, 971-982. Eysenck, H.J. (1952). The effects of psychotherapy: An evaluation. Journal of Consulting Psychology, 16, 319-324. Fennell, M.J.V., & Teasdale, J.D. (1987). C ognitive therapy for depression: Individual differences and the process of change. Cognitive Therapy and Research, 11, 253271. Finch, A.E., Lambert, M.J., & Schaalje, B. G. (2001). Psychotherapy quality control: The statistical generation of exp ected recovery curves for integration into an early warning system. Clinical Psychology and Psychotherapy 8, 231-242. Firth-Cozens, J., & Hardy, G.E. (1992) Occ upational stress, clin ical treatment and changes in job perceptions. Journal of Occupational and Organizational Psychology, 65, 81-88.

PAGE 116

106 Flaskerud, J.H. & Liu, P.Y. (1991). Effects of an Asia n client-therapist language, ethnicity and gender match on utili zation and outcome of therapy. Community Mental Health Journal 27, 31-42. Fletcher, B.C., & Payne, R. L. (1980a). St ress at work: A review and theoretical framework, Part 1. Personnel Review, 9, 19-29. Fletcher, B.C., & Payne, R. L. (1980b). Stre ss at work: A review and theoretical framework, Part 2. Personnel Review, 9, 5-8. Foley, S.H., O’Malley, S., Rounsaville, B., Pr usoff, B.A., & Weissman, M.M. (1987). The relationship of client difficulty to therapist performance in interpersonal psychotherapy of depression. Journal of Affective Disorders 12, 207-217. Frank, J.D., & Frank, J.B. (1991). Persuasion and healing (3rd ed.). Baltimore: Johns Hopkins University Press. Fujino, D.C. Okazaki, S ., & Young, K (1994). Asian-American women in the mental health system: An examination of ethnic and gender match between therapist and client. Journal of Community Psychology 22, 164-176. Garfield, S.L. (1978). Research on client variab les in psychotherapy. In A.E. Bergin & S. L. Garfield (Eds.), Handbook of psychotherapy and behavior change (2nded., pp. 191-232). New York: Wiley. Garfield, S.L. (1986). Research on client vari ables in psychotherapy. In S.L. Garfield & A.E. Bergin (Eds.), Handbook of psychotherapy and behavior change (3rd ed., pp. 213-256). New York: Wiley. Garfield, S.L. (1994). Research on client variab les in psychotherapy. In A.E. Bergin & S. L. Garfield (Eds.), Handbook of psychotherapy and behavior change (4th ed., pp. 190-228). New York: Wiley. Garfield, S.L. (1996). Some problems with “validated ” forms of psychotherapy. Clinical Psychology: Science & Practice 3, 218-229. Gaston, L., Marmar, C.R., Gallagher, D ., & Thompson, L.W. (1989). Impact of confirming patient expectation of change processes in behavioral, cognitive, and brief dynamic psychotherapy. Psychotherapy, 26, 296-302. Gaston, L., Marmar, C.R., Thompson, L.W., & Gallagher, D. (1988). Relation of patient pretreatment characteristics to the therap eutic alliance in diverse psychotherapies. Journal of Consulting and Clinical Psychology, 36, 483-489. Gatchel, R.J., Polatin, P.B., & Kinney, R.K. (1995). Predicting outcome o chronic back pain using clinical predictors of psychopathology: A prospective analysis. Health Psychology, 14, 415-420.

PAGE 117

107 Gelhart, R.O. Hand-Ronga, N., & King, H.L. (2002). Group cognitive-behavioral treatment of depression and the interaction of demographic variables. Journal of Cognitive Psychotherapy, 16, 469-486. George, L., Blazer, D.G., & Hughes, D.C., & Fowler, N. (1989). So cial support and the outcome of major depression. British Journal of Psychiatry, 154, 478-485. Ginexi, E.M., Foss, M.A., & Scott, C.K. (2003). Transition from treatment to work: Employment patterns following publicly funded substance abuse treatment. Journal of Drug Issues, 33, 497-518. Glass, G.V. (2001). Foreward. In B.E. Wampold, The great psychotherapy debate: Models, methods and findings (pp. ix-x). Mahwah, N.J.: Lawrence Erlbaum Associates. Gleaves, D.H., & Eberenz, K.P. (1994). Sexua l abuse histories among treatment-resistant bulimia nervosa patients. International Journal of Eating Disorders, 15, 227-231. Gomes-Schwartz, B. (1978). Effective ingred ients in psychotherapy: Predictions of outcome from process variables. Journal of Consulting and Clinical Psychology, 46, 1023-1035. Gonzales, L.R., Lewinsohn, & Clarke, G.N. (1985). Longitudinal follow-up of unipolar depressives: An investigation of predictors of relapse. Journal of Consulting and Clinical Psychology, 53, 461-469. Gore, S. (1978). The effect of social support in moderating the health consequences of unemployment. Journal of Health and Social Behavior, 19, 157-165. Gottschalk, L.A., Mayerson, P., & Gottlieb, A. A. (1967). Prediction and evaluation of outcome in an emergency brief psychotherapy clinic. The Journal of Nervous and Mental Disease 144, 77-96. Greenberger, E., & O’Neil, R. (1993). Spouse, parent, worker role commitments and role-related experiences in the construction of adults’ well-being. Developmental Psychology, 29, 181-197. Haas, E., Hill, R.D., Lambert, M.J., & Mo rrell, B. (2002). Do early responders to psychotherapy maintain treatment gains. Journal of Clinical Psychology 58, 11571172. Henderson, A.S. (1984). Interpreting the evidence on social support. Social Psychiatry, 19, 49-52. Hill, C.E. & Lambert, M.J. (2004). Met hodological issues in studying psychotherapy processes and outcomes. In M.J., Lambert (Eds.), Bergin & Garfield’s Handbook of psychotherapy and behavior change (5th ed.) (pp. 84-136). New York: John Wiley & Sons.

PAGE 118

108 Hoberman, H.M., Lewinsohn, & Tilson, M. ( 1988). Group treatment of depression: Individual predictors of outcome. Journal of Consulting an d Clinical Psychology, 36, 393-398. Holt, R.R. (1982). Occupational stress. In L. Goldberger and S. Breznitz (Eds.), Handbook of stress: Theoreti cal and clinical aspects (pp. 419-444). New York: Free Press. Hooley, J.M., Orley, J., & Teasdale, J.D. (1986) Levels of expresse d emotion and relapse in depressed patients. British Journal of Psychiatry, 148, 642-647. Hooley, J.M., & Teasdale, J.D. (1989). Predic tors of relapse in unipolar depressives: Expressed emotion, marital distre ss, and perceived criticism. Journal of Abnormal Psychology 98, 229-235. Horowitz, L.M, Rosenberg, S.E., Baer, B.A., Ureno, G., & Villesenor, V.S. (1988). Inventory of Interpersonal Problems: Psychometric properties and clinical applications. Journal of Personality and Social Psychology 61, 68-79. Howard, K. I., Kopta, S. M., Krause, M. S., & Orlinsky, D. E. (1986). The dose-effect relationship in psychotherapy. American Psychologist 41, 159-164. Howard, K. I., Moras, K., Brill, P. L., Mart inovich, Z., & Lutz, W. (1996). Evaluation of psychotherapy: Efficacy, effectiv eness, and patient progress. American Psychologist 51, 1059-1064. Hubble, M. A., Duncan, B. L., & Miller, S. D. (1999a). Introduction. In M. A. Hubble, B. L. Duncan, & S. D. Miller (Eds.), The heart and soul of c hange: What works in therapy (pp. 1-19). Washington, DC: Amer ican Psychological Association. Hubble, M. A., Duncan, B. L., & Miller, S. D. (1999b). The heart and soul of change: What works in therapy Washington, DC: American Psychological Association. Hubble, M. A., Duncan, B. L., & Miller, S. D. (1999c). Directi ng attention to what works. In M. A. Hubble, B. L. Duncan, & S. D. Miller (Eds.), The heart and soul of change: What works in therapy (pp. 407-447). Washington, DC: American Psychological Association. Ilardi, S.S., & Craighead, W.E. (1994). The role of nonspecific factors in cognitivebehavior therapy for depression. Clinical Psychology: Science and Practice, 1, 138-156. Ilardi, S.S., & Craighead, W. E. (1999). Commentary. Rapi d early response, cognitive modification, and nonspecific factors in c ognitive behavior therapy for depression: a reply to Tang and DeRubeis. Clinical Psychology: Science and Practice 6, 295299.

PAGE 119

109 Jackson, D. (1984). Personality Research Form manual (3rd ed.). Port Huron, MI: Research Psychologists Press. Jacobson, N.S., & Truax, P. (1991). Clinical significance: A statistical approach to defining meaningful change in psychotherapy research. Journal of Consulting and Clinical Psychology, 59, 12-19. Johnson, L.D., & Shaha, S. (1996). Improving quality in psychotherapy. Psychotherapy 33, 225-236. Kadera, S.W., Lambert, M.J., & Andrews, A.A. (1996). How much therapy is really enough: A session-by-session analysis of the psychotherapy dose-effect relationship. Psychotherapy: Research and Practice 5, 132-151. Karasek, R.A. (1979). Job demands, job decision latitude, and mental strain: Implications for job redesign. Administrative Science Quarterly, 24, 285-308. Keller, M.B., Shapiro, R.W., Lavori, P.W ., & Wolfe, N. (1982). Relapse in major depressive disorder. Archives of General Psychiatr y, 39, 911-915. Kelly, P.J., Blacksin, B., & Mason, E. ( 2001). Factors affecting substance abuse treatment completion for women. Issues in Mental Health Nursing, 22, 287-304. Kerns, R.D., & Jacob, M.C. (1993). Psychological aspects of back pain. Bailliere’s Clinical Rheumatology, 7, 337-356. Koenigsberg, H.W., & Handley, R. (1986). Expr essed emotion: From predictive index to clinical construct. American Journal of Psychiatry, 143, 1361-1373. Krantz, S.E., & Moos, R. H. (1988). Risk f actors at intake predict nonremission among depressed patients. Journal of Consulting and Clinical Psychology, 56, 863-869. Laffaye, C., Kennedy, C., & Stein, M.B. (2003) Post-traumatic stress disorder and health-related quality of life in female victims of intimate partner violence. Violence and Victims, 18, 227-238. Lambert, M.J. (1976). Spontaneous remission in adult neurotic diso rders: A revision and summary. Psychological Bulletin, 83, 107-119. Lambert, M. J. (1992). Psychotherapy outcome research: Implications for integrative and eclectic therapists. In J.C. Norcross & M.R. Goldfried (Eds.), Handbook of psychotherapy integration (pp. 94-129). New York: Basic Books. Lambert, M.J. (2001). Psychotherapy outcome and quality improvement: Introduction to the special section on pa tient-focused research. Journal of Consulting and Clinical Psychology 69, 147-149.

PAGE 120

110 Lambert, M.J. (Ed.). (2004). Bergin & Garfield’s Handbook of psychotherapy and behavior change (5th edition). New York: John Wiley & Sons. Lambert, M.J., & Bergin, A.E. (1994). The effectiveness of psychotherapy. In A.E. Bergin & S.L. Garfield (Eds.), Handbook of psychotherapy and behavior change (4th ed., pp. 143-189). New York: Wiley. Lambert, M. J., & Cattani-Thompson, K. (1996). Current findings regarding the effectiveness of counseling: Implications for practice. Journal of Counseling and Development 74, 601-608. Lambert, M. J., Hansen, N. B., & Finch, A. E. (2001). Patient-focused research: Using patient outcome data to enhance treatment effects. Journal of Consulting and Clinical Psychology, 69, 159-172. Lambert, M. J., Hansen, N. B., Umpress, V., Lunnen, K., Okiishi, & Burlingame, G. (2003). Administration and scoring manual for the Outcome Questionnaire (OQ45.2). American Professional Credentialing Services LLC. Lambert, M.J., & Hill, C.E. (1994). Assessing psychotherapy outcomes and processes. In A. E. Bergin & S.L. Garfield (Eds.), Handbook of psychotherapy and behavior change (4th ed., pp.72-113), New York: Wiley. Lambert, M. J., Lunnen, K., Umpress, V. Hansen, N. B., & Burlingame, G. (1994). Administration and scoring manual for the Outcome Questionnaire (OQ-45.1). Salt Lake City, UT: IHC Center for Behavioral Healthcare Efficacy. Lambert, M.J. & Ogles, B.M. (2004). The effi cacy and effectiveness of psychotherapy. In M.J., Lambert (Eds.), Bergin & Garfield’s Handb ook of psychotherapy and behavior change (5th ed., pp. 139-193). New York: John Wiley & Sons. Lambert, M. J., Whipple, J.L., Bishop, M.J ., Vermeersch, D.A., Gray, G.V., & Finch, A. E. (2002). Comparison of empirically-deriv ed and rationally-derived methods for identifying patients at risk for treatment failure. Clinical Psychology and Psychotherapy, 9, 149-164. Landerman, R., George, L., Campbell, R.T., & Blazer, D.G. (1989). Alternative models of the stress buffering hypothesis. American Journal of Community Psychology, 17, 625-642. Leibert, T.W., Archer, J.A., Munson, J., & Yor k, G. (in press). An exploratory study of client perceptions of Internet couns eling and the therapeutic alliance. Journal of Mental Health Counseling Lewinsohn, P.M., Zeiss, A.M., Duncan, E.M. (1989). Probability of relapse after recovery from an episode of depression. Journal of Abnormal Psychology, 98, 107116.

PAGE 121

111 Longabaugh, R., Beattie, M., Noel, N., Stout, R., & Malloy, P. (1993) The effect of social investment on treatment outcome. Journal of Studies on Alcohol, 54, 465-478. Luborsky, L., Chandler, M., Auerbach, A.H., Cohen, J., & Bachrach, H. (1971). Factors influencing the outcome of psychotherapy: A review of quantitative research. Psychological Bulletin, 75, 145-185. Luborsky, L., Crits-Christoph, P., Mi ntz, J., & Auerbach, A. (1988). Who will benefit from psychotherapy? Predic ting therapeutic outcomes. NY: Basic Books. Luborsky, L., & DeRubeis, R.J. (1984). The use of psychotherapy treatment manuals: A small revolution in psychot herapy research style. Clinical Psychology Review 4, 514. Luborsky, L., McLellan, A. T., Woody, G.E ., O’Brien, C.P., & Auerbach, A. (1985). Therapist success and its determinants. Archives of General Psychiatry, 42, 602611. Luborsky, L., Singer, B., & Luborsky, L. (1975). Comparative studies of psychotherapies. Archives of General Psychiatry, 32, 995-1008. Lunnen, K.M., & Ogles, B.M. (1998). A multi-pe rspective, multi-variable evaluation of reliable change. Journal of Consulting and Clinical Psychology 66, 400-410. Magni, G., Moreschi, C., Rigatti-Luchini, S ., & Merskey, H. (1994). Prospective study on the relationship between depr essive symptoms and chronic musculoskeletal pain. Pain, 56, 289-297. Mann, A.H., Jenkins, R., & Belsey, E. (1981). The twelve-month outcome of patients with neurotic illness in general practice. Psychological Medicine, 11, 535-550. McCaul, M.E., Svikis, D.S., & Moore, R.D. (2001). Predictors of outpatient treatment retention: Patient versus substance use characteristics. Drug and Alcohol Dependence, 62, 9-17. McConnaughy, E.N., DiClemente, C.C., Prochaska, J.O., & Velicer, W.F. (1989). Stages of change in psychotherapy: A follow-up report. Psychotherapy, 26, 494-503. McConnaughy, E.N., Prochaska, J.O., & Velicer W.F. (1983). Stages of change in psychotherapy: Measurement and sample profiles. Psychotherapy: Theory, Research and Practice 20, 368-375. Mermelstein, R.J., Lichtenstein, E., & McInty re, K. (1983). Partner support and relapse in smoking cessation programs. Journal of Consulting and Clinical Psychology, 51, 465-466.

PAGE 122

112 Meyer, B., Pilkonis, P.A., Krupnick, J.L., Egan, M.K., Simmens, S.J., & Sotsky, S.M. (2002). Treatment expectancies, patient al liance, and outcome further analyses from the National Institute of Ment al Health Treatment of Depression Collaborative Research Program. Journal of Consulting an d Clinical Psychology, 70, 1051-1055. Miller, S.D., Duncan, B.L., & Johnson, L.D. (1999). The verdict is the key. Family Therapy Networker 23, 46-55. Mi-Young, J. (2001). Study on the correlation between depression and quality of life for Korean women. Nursing and Health Sciences, 3, 131-137. Mohr, D.C. (1995). Negative outcome in psychotherapy. Clinical Psychology Review 2, 1-27. Mohr, D.C., Beutler, L.E., Engle, D., Shoha m-Salomon, V., Bergan, J., Kaszniak, A.W. & Yost, E. (1990). Identification of patie nts at risk for non-response and negative outcome in psychotherapy. Journal of Consulting and Clinical Psychology 58, 622-628. Moos, R.H. (1990). Depressed outpatients’ life contexts, amount of treatment, and treatment outcome. The Journal of Nervous and Mental Disease, 178(2), 105112. Myers, J.L., & Well, A.D. (1991). Research design and statistical analysis New York: Harper Collins. Murphy, E. (1983). The prognosis of depression in old age. British Journal of Psychiatry, 142, 111-119. Nathan, P.E. (1998). Practice guidelines not yet an ideal. American Psychologist 53, 290-299. Ogles, B. M., Lambert, M.J., & Fields, S.A. (2002). Essentials of outcome assessment New York: John Wiley & Sons. Okasha, A., El Akabawi, A.S., Snyder, K.S., Wilson, A.K., Youssef, I., & El Dawla, A.S. (1994). Expressed emotion, perceived cri ticism, and relapse in depression: A replication in an Egyptian community. American Journal of Psychiatry, 151, 10011005. Orlinsky, D.E., Grawe, K., & Parks, B.K. (1994). Process and outcome in psychotherapynoch einmal. In A.E. Bergin & S. L. Garfield (Eds.), Handbook of psychotherapy and behavior change (4th ed., pp. 270-376). New York: Wiley. Oswald, A.J. (1997). Happiness and economic performance. The Economic Journal, 107, 1815-1831.

PAGE 123

113 Paul, G. L. (1967). Strategy of out come research in psychotherapy. Journal of Consulting and Clinical Psychology, 31, 109-118. Paul, G. L. (1969). Behavior modification research: Design an d tactics. In C.M. Franks (Ed.), Behavior therapy: Appraisal and status (pp. 29-62). New York: McGrawHill. Paykel, E.S., Cooper, Z., Ramana, R., & Hayhur st, H. (1996). Life ev ents, social support and marital relationships in th e outcome of severe depression. Psychological Medicine, 26, 121-133. Pedhazur, E.J. (1982). Multiple regression in behavioral research: Explanation and prediction (2nd ed.). Fort Worth, TX: Holt, Rinehart, & Winston. Petry, N.M., Tennen, H., & Affleck, G. (2000). Stalking the elusive cl ient variable in psychotherapy research. In C.R. Snyder & R.E. Ingram (Eds.), Handbook of psychological change: Psychotherapy processes and practices for the 21st century (pp. 88-108). New York, NY: John Wiley & Sons. Plante, T.G., Couchman, C.E., & Hoffman, C.A. (1998). Measuring treatment outcome and client satisfaction among children and families: A case report. Professional Psychology: Research and Practice 29, 52-55. Platt, J.J. (1995). Vocational re habilitation of drug abusers. Psychological Bulletin, 117, 416-433. Platt, S., & Kreitman, N. (1985). Para-s uicide and unemployment among men in Edinburgh 1968-82. Psychological Medicine 291, 1563-1566. Polatin, P.B., Kinney, R.K., Gatchel, R.J., L illo, E., & Mayer, T.G. (1993). Psychiatric illness and chronic low-back pain. Spine, 18, 66-71. Prochaska, J.O. (1999). How do people change and how can we change to help many more people? In M. A. Hubble, B. L. Duncan, & S. D. Miller (Eds.), The heart and soul of change: What works in therapy (pp. 227-255). Washington, DC: American Psychological Association. Prochaska, J.O., & DiClemente, C.C. (1983). Stages and processes of self-change of smoking: Toward an integrative model of change. Journal of Consulting and Clinical Psychology 51, 390-395. Prochaska, J.O., DiClemente, C.C., Velicer W.F., & Rossi, J.S. (1993). Standardized, individualized, interactive, and persona lized self-help prog rams for smoking cessation. Health Psychology, 12, 399-405. Prochaska, J., Norcross, J., Fowler, J., Fo llick, M., & Abrams, D. (1992). Attendance and outcome in a worksite weight control progr am: Processes and stages of change as process and predictor variables. Addictive Behaviors 17, 35-45.

PAGE 124

114 Project MATCH Research Group. (1997). Matc hing alcoholism treatments to client heterogeneity: Project MATCH pos ttreatment drinking outcomes. Journal of Studies on Alcohol, 58, 7-29. Rainer, J.P. (1996). The pragmatic rele vance and methodological concerns of psychotherapy outcome research related to co st effectiveness and cost-offset in the emerging health care environment. Psychotherapy 33, 216-224. Ray, W.J. (2000). Methods: Toward a science of behavior and experience (6th ed.). Belmont, CA: Wadsworth. Renshaw, K.D., Chambless, D.L., & Steketee, G. (2001). Comorbidity fails to account for the relationship of expressed emoti on and perceived criticism to treatment outcome in patients with anxiety disorders. Journal of Behavior Therapy and Experimental Psychiatry, 32, 145-158. Renshaw, K.D., Chambless, D.L., & Steketee G. (2003). Perceived criticism predicts severity of anxiety symptoms after behavi oral treatment in patients with obsessivecompulsive disorder and panic disorder with agoraphobia. Journal of Clinical Psychology 59, 411-421. Riso, L.P., Klein, D.N., Anderson, R.L., Ouimette, P.C., & Lizardi, H. (1996). Convergent and discriminant validity of perceived criticism from spouses and family members. Behavior Therapy, 27, 129-137. Robinson, L.A., Berman, J.S., & Neimeyer, R. A. (1990). Psychotherapy for the treatment of depression: A comprehensive revi ew of controlled outcome research. Psychological Bulletin, 100, 30-49. Rodgers, C.S., & Norman, S.B. (2004). Considering PTSD in the treatment of female victims of intimate partner violence. Psychiatric Times, 21, 68-71. Rosenzweig, S. (1936). Some implicit co mmon factors in diverse methods of psychotherapy: “At last the Dodo said, ‘Everybody has won and all must have prizes.’” American Journal of Orthopsychiatry, 6, 412-415. Rounsaville, B.J., Weissman, M.M., & Pr usoff, B.A. (1981). Psychotherapy with depressed outpatients: Patient and proce ss variables as predictors of outcome. British Journal of Psychiatry, 138, 67-74. Rude, S.S., & Rehm, L.P. (1991). Response to treatments for depression: The role of initial status on targeted cogni tive and behavioral skills. Clinical Psychology Review 11, 493-514. Rudy, T.E., Kerns, R.D., & Turk, D.C. (1988) Chronic pain and depression: Toward a cognitive-behavioral mediation model. Pain, 35, 129-140.

PAGE 125

115 Safren, S.A., Juster, H.R., & Heimberg, R.G. (1997). Clients’ expectations and their relationship to pretreatment symptomato logy and outcome of cognitive-behavioral group treatment for social phobia. Journal of Consulting and Clinical Psychology, 65, 694-698. Sansone, R.A., Whitecar, P., Meier, B.P ., & Murry, A. (2001). The prevalence of borderline personality among primary care patients with chronic pain. General Hospital Psychiatry, 23, 193-197. Seibel, C.A., & Dowd, E.T. (1999). R eactance and therapeutic noncompliance. Cognitive Therapy and Research, 23, 373-379. Shapiro, D.A., & Shapiro, D. (1982). Meta-a nalysis of comparative therapy outcome studies: A replication and refinement. Psychological Bulletin 92, 581-604. Sherbourne, C.D., Hays, R.D., & Wells, K.B. (1995). Personal and psychosocial risk factors for physical and mental health outcomes and course of depression among depressed patients. Journal of Consulting and Clinical Psychology, 63, 345-355. Sherrington, J. M., Hawton, K., Fagg, J., A ndrew, B., & Smith, D. (2001). Outcome of women admitted to hospital for depressive illness: F actors in the prognosis of severe depression. Psychological Medicine, 31, 115-125. Silverman W. H. (1996). Cookbooks, Manuals, and paint-by-numbers: Psychotherapy in the 90’s. Psychotherapy 33, 207-215. Smith, M.L., & Glass, G.V. (1977). Meta-a nalysis of psychotherapy outcome studies. American Psychologist 32, 752-760. Smith, M.L., Glass, G.V., & Miller, T.I. (1980). The benefits of psychotherapy Baltimore: John Hopkins University Press. Snyder, C.R., Michael, S.T., & Cheavens, J.S. (1999). Hope as a psychotherapeutic foundation of common factors, placebos, and e xpectancies. In M. A. Hubble, B. L. Duncan, & S. D. Miller (Eds.), The heart and soul of change: What works in therapy (pp. 179-200). Washington, DC: Am erican Psychological Association. Sotsky, S.M., Glass, D.R., Shea, T.M., Pilkonis, P.A., Collins, J.F., Elkin, I., Watkins, J.T., Imber, S.D., Leber, W.R., Moyer, J., & Oliveri, M.E. (1991). Patient predictors of response to psychothera py and pharmacotherapy: Findings in the NIMH treatment of depression collaborative research program. American Journal of Psychiatry, 148, 997-1008. Spanier, G. (1976). Measuring dyadic adjustment : New scales for assessing the quality of marriage and similar dyads. Journal of Marriage and the Family 38, 15-28. Spiegel, D., & Wissler, T. (1986). Family e nvironment as a predictor of psychiatric rehospitalization. American Journal of Psychiatry, 143, 56-60.

PAGE 126

116 Spielberger, C.D., Gorsuch, R.L., & Lushene, R.E. (1970). The State-Trait Anxiety Inventory Self-Evaluation Questionnaire Palo Alto, CA: Consulting Psychologists Press. Spitzer, R.L., Endicott, J., & Robins, E. (1978). Research diagnostic criteria. Archives of General Psychiatry, 36, 773-782. Stafford, E.M., Jackson, P.R., & Banks, M. H. (1980). Employment, work involvement and mental health in less qualified young people. Journal of Occupational Psychology, 53, 291-304. Steinmetz, J.L., Lewinsohn, P.M., & Antonucci o, D.O. (1983). Pred iction of individual outcome in a group intervention for depression. Journal of Consulting and Clinical Psychology, 51, 331-337. Stiles, W.B., Shapiro, D.A., & Elliott, R. ( 1986). “Are all psychotherapies equivalent?” American Psychologist, 41, 165-180. Stoolmiller, M., Duncan, T., Bank, L., & Pa tterson, G.R. (1993). Some problems and solution in the study of change: Signifi cant patterns in client resistance. Journal of Consulting and Clinical Psychology 61, 920-928. Strupp, H.H., Wallach, M.S., Wogan, M., & Je nkins, J.W. (1963). Psychotherapists’ assessments of former patients. Journal of Nervous and Mental Disease, 137, 222230. Surtees, P.G. (1980). Social support, resi dual adversity and depressive outcome. Social Psychiatry, 15, 71-80. Tait, M., Padgett, M.Y., & Baldwin, T.T. (1989) Job and life satisfaction: A reevaluation of the strength of the relati onship and gender effects as a function of the date of the study. Journal of Applied Psychology 74, 502-507. Tallman, K., & Bohart, A.C. (1999). The clie nt as a common factor: Clients as selfhealers. In M. A. Hubble, B. L. Duncan, & S. D. Miller (Eds.), The heart and soul of change: What works in therapy (pp. 91-132). Washington, DC: American Psychological Association. Tang, T.Z., & DeRubeis, R.J. (1999). Reconsid ering rapid early response in cognitive behavioral therapy for depression. Clinical Psychology: Science and Practice, 6, 283-288. Tompson, M.C., Goldstein, M.J., Lebell, M.B., Mintz, L.I., Marder, S.R. & Mintz, J. (1995). Schizophrenic patients’ percepti ons of their relatives’ attitudes. Psychiatry Research, 57, 155-167. Vaillant, G.E. (1988). What can long-term follow-up teach us about relapse and prevention of relapse in addiction? British Journal of Addiction, 83, 1147-1157.

PAGE 127

117 Vaughn, C.E., & Leff, J.P. (1976). The influenc e of family and social factors on the course of psychi atric illness. British Journal of Psychiatry, 129, 125-137. Wampold, B.E. (2001). The great psychotherapy debate: Models, methods and findings Mahwah, N.J.: Lawrence Erlbaum Associates. Wampold, B.E., Mondin, G.W., Moody, M., S tich, F., Benson, K., & Ahn, H. (1997). A meta-analysis of outcome studies co mparing bona fide psychotherapies: Empirically, “All must have prizes.” Psychological Bulletin, 122, 203-215. Ware, J.E. (1999). SF-36 health survey. In M.E. Maruish (Ed.), The use of psychological testing for treatment pla nning and outcomes assessment (2nd ed., pp. 1227-1246). Mahwah, NJ: Lawrence Erlbaum Associates. Ware, J.E., Kosinski, M., & Keller, S.D. (1994). SF-36 Physical and Mental Health Summary scales: A user’s manual. Boston: The Health Institute. Ware, J.E., Kosinski, M., & Keller, S.D. (1996). A 12-item short-form health survey: Construction of scales and prelimin ary tests of reliability and validity. Medical Care, 34, 220-233. Ware, J.E., Kosinski, M., Turner-Bowker, D.M., & Gandek, B. (2002). How to score version 2 of the SF-12 health survey : With a supplement documenting version 1 Lincoln, RI: QualityMetric. Warr, P.B. (1978). A study of psychological well-being. British Journal of Psychology, 69, 111-121. Weissman, M.M., & Bothwell, S. (1976). Asse ssment of social adjustment by patient self-report. Archives of General Psychiatry 33, 1111-1115. Whipple, J.L., Lambert, M.J., Vermeersch, D.A., Smart, D.W., Nielsen, S.L., & Hawkins, E.J. (2003). Improving the effects of psychot herapy: The use of early identification of treatment and problem-solving st rategies in routine practice. Journal of Counseling Psychology 50, 59-68. Wierzbicki, M., & Pekarik, G. (1993). A meta-analysis of psychotherapy dropout. Professional Psychology: Research and Practice 24, 190-195. Willer, B., & Miller, G. H. (1978). On the relationship of client satisfaction to client characteristics and outcome of treatment. Journal of Clinical Psychology 34(1), 157-160. Wilson, T.G. (1999). Rapid response to cognitive behavior therapy. Clinical Psychology: Science & Practice 6, 289-292. Wilson, K.G., Eriksson, M.Y., D’Eon, J.L., Mi kail, S.F., & Emery, P.C. (2002). Major depression and insomnia in chronic pain. Clinical Journal of Pain, 18, 77-83.

PAGE 128

118 Wilson, T.G., Fairburn, C.C., Agras, S. W., Walsh, T.B., & Kraemer, H. (2002). Cognitive-behavioral therapy for bulimia nervosa time courses and mechanisms of change. Journal of Consulting and Clinical Psychology, 70, 267-274.

PAGE 129

119 BIOGRAPHICAL SKETCH Todd William Leibert was born October 8, 1964, in Syracuse New York, the last of the three children. His parents, Bob and Di xie, moved to Kansas City, Missouri, the following year where his father assumed a pos ition at the University of Missouri as a Professor of Reading Education. In high school, Todd’s primary academic interest was art and one painting won a national award and was displayed in a Ne w York art show. He did not engage academically until his mid-20s when he pursued psychology as his major and anthropology as his minor. He earned his B.A. in 1991. Intrigued with human consciousness, he continued hi s education in a PhD program in experimental psychology at the University of South Fl orida in Tampa. He studied hum an memory for nearly three years, and earned a master’s degree, despite realizing that it was not the career for him. His interest had grown away from the in ternal workings of the human mind to interpersonal relations and personality. He had volunteered on a suicide hotline and had applied to the University of Florida in Gainesville in the Counselor Education Department. He earned his M.E.D./ED.S. in Decem ber of 1998 and in the spring of 1999 became a nationally certified counselor. He worked toward mental health counseling licensure in Florida working with male, ad olescent sex offenders and later, adult substance dependence in-patient clients. He returned for his PhD in counselor education in the fall of 2001 and soon after acquired his licensure. His aim was to blend his earlier

PAGE 130

120 interests in research with his newer interest s in mental health. He has developed a strong interest in promoting the re spect of the field of counse ling through empirical research. His dissertation marks the first step in his inte rest to further describe what factors make mental health counseling so benefici al to people in the community.