Citation
Jail Diversion and Recidivism

Material Information

Title:
Jail Diversion and Recidivism Impact on Community Integration and Treatment Utilization
Creator:
Rivera, Shinlay Chu
Place of Publication:
[Gainesville, Fla.]
Publisher:
University of Florida
Publication Date:
Language:
english
Physical Description:
1 online resource (1 p.)

Thesis/Dissertation Information

Degree:
Doctorate ( Ph.D.)
Degree Grantor:
University of Florida
Degree Disciplines:
Marriage and Family Counseling
Human Development and Organizational Studies in Education
Committee Chair:
Daniels, Marion Harry
Committee Members:
Miller, David
Sherrard, Peter Andrew
Harman, Jeffrey Scott
Graduation Date:
5/4/2013

Subjects

Subjects / Keywords:
Criminal justice ( jstor )
Criminal punishment ( jstor )
Criminals ( jstor )
Housing ( jstor )
Jails ( jstor )
Mental disorders ( jstor )
Mental health ( jstor )
Prisons ( jstor )
Recidivism ( jstor )
Substance abuse ( jstor )
Human Development and Organizational Studies in Education -- Dissertations, Academic -- UF
diversion -- recidivism
Alachua County ( local )
Genre:
Electronic Thesis or Dissertation
born-digital ( sobekcm )
Marriage and Family Counseling thesis, Ph.D.

Notes

Abstract:
The increasing number of inmates in jail and prison has prompted an examination of individuals in need of treatment for mental health illnesses and/or substance abuse issues.  Diversion programs have demonstrated effectiveness by reducing re-arrests post-diversion and linking individuals to treatment.  This study utilized secondary data collected from a local jail diversion program.  The purpose of this study was to investigate variables affecting recidivism including: diversion participation, treatment utilization, mandated treatment, length of treatment, linkages to social support services, and criminal history.  Study participants were classified into three groups: program completers, discharged, and non-participants. The study hypotheses were tested using ANOVA, chi-square, and regression analyses.  Study participants (N=283) were referred and accepted to the diversion program between July 2008 and March 2011.  The sample was predominantly males (N=209, 73.9%), and was represented by mostly African Americans and Caucasians. Participants reported having mental health diagnoses and substance abuse issues.  The three status groups were tested for pre-diversion differences in age, gender, ethnicity, educational level, mental health diagnoses, substance abuse disorders, number of arrests, number of jail days, and severity of charges.  Significant differences were found for ethnicity, diagnoses, pre-diversion arrests, and pre-diversion jail days.  No differences were found in post-diversion criminal involvement and treatment service utilization for court mandated/non-mandated participants.  Other findings indicated program participants had a higher average of post-diversion arrests and jail days, as well as a higher treatment utilization rate, than non-participants.  Significant relationships were found for length of time in treatment and linkage to social support services, specifically housing, for post-diversion arrests only, and not jail days or severity of charges.  This study also suggested a different interpretation of the severity of charge variable, calculated as change in severity.  A discussion of the study’s results, limitations of the study, and implications for future research, theory, and practice were presented. ( en )
General Note:
In the series University of Florida Digital Collections.
General Note:
Includes vita.
Bibliography:
Includes bibliographical references.
Source of Description:
Description based on online resource; title from PDF title page.
Source of Description:
This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Thesis:
Thesis (Ph.D.)--University of Florida, 2013.
Local:
Adviser: Daniels, Marion Harry.
Electronic Access:
RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2015-05-31
Statement of Responsibility:
by Shinlay Chu Rivera.

Record Information

Source Institution:
University of Florida
Holding Location:
University of Florida
Rights Management:
Copyright by Shinlay Chu Rivera. Permission granted to University of Florida to digitize and display this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
Embargo Date:
5/31/2015
Resource Identifier:
884960941 ( OCLC )
Classification:
LD1780 2013 ( lcc )

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1 JAIL DIVERSION AND RECIDIVISM: IMPACT ON COMMUNITY INTEGRATION AND TREATMENT UTILIZATION By SHINLAY CHU RIVERA A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQ UIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2013

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2 2013 Shinlay Chu Rivera

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3 To those fighting for a chance at choice, hope, and recovery

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4 ACKNOWLEDGMENTS I would first like to express my immense gratitude to the members of my graduate committee who stayed committed in seeing me through this process I am honored and humbled by their support throughout this long journey I would like to first thank Dr. Harry Daniels, for hi s patience and guidance as my committee chair. I would also like to thank Dr. Peter Sherrard for his unwavering support and encouragement. I would also like to thank Dr. David Miller and Dr. Jeffrey Harmon for taking the time to read through my numerous drafts and offering their feedback and recommendations. The idea and passion for this project would not be possible without the opportunities and experience s extended to me from working at Meridian Behavioral Healthcare. I am truly grateful for everythi ng I have learned from both my coworkers and clients I have worked with through the years. I would like to thank Betsy Pearman for her guidance and words of encouragement. I could not imagine finishing this process without her! I would also like to reco for their friendship, advice, and wisdom I am so grateful for my family, for being my biggest fans and for believing in me. I want to thank my mother for her unconditional love, and for teaching me the value of hard work Personally this journey would not have been possible without the love and support of my husband, Eliseo Rivera. He instilled in me strength and perseverance providing me with encouragement that I am always one step further than I was Last but not le ast, I would like to thank the University of Florida for an amazing fifteen years of education, experiences, friends, and colleagues Go Gators!!!

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5 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ .. 4 LIST OF TABLES ................................ ................................ ................................ ............ 7 LIST OF FIGURES ................................ ................................ ................................ .......... 8 LIST OF TERMS ................................ ................................ ................................ ............. 9 ABSTRACT ................................ ................................ ................................ ................... 10 CHAPTER 1 INTRODUCTIO N ................................ ................................ ................................ .... 12 Statement of the Problem ................................ ................................ ....................... 13 Criminalization ................................ ................................ ................................ .. 14 Jail and Prison Capacities ................................ ................................ ................ 15 Treatment Needs ................................ ................................ .............................. 16 Treatment Utilization ................................ ................................ ........................ 18 Theoretical Framework ................................ ................................ ........................... 19 Self Control Theory ................................ ................................ .......................... 20 Therapeutic Alliance and Readiness ................................ ................................ 21 Need for the Study ................................ ................................ ................................ .. 24 Purpose of the study ................................ ................................ ............................... 25 Research Questions ................................ ................................ ............................... 26 2 LITERATURE REVIEW ................................ ................................ .......................... 28 Mental Illness in the Jail and Prison Systems ................................ ......................... 29 Diversion Programs ................................ ................................ ................................ 30 General Characteristics of Diversio n Participants ................................ ............ 33 Effectiveness of Jail Diversion Programs ................................ ......................... 34 Post Diversion Treatment Utilization ................................ ................................ 35 Different Programs and Initiatives ................................ ................................ ........... 36 Forensic Diversion Program ................................ ................................ ............. 36 Goals of the Forensic Diversion Program ................................ ......................... 38 Summary ................................ ................................ ................................ ................ 39 3 METHODOLOGY ................................ ................................ ................................ ... 42 Research Method ................................ ................................ ................................ ... 43 Study Setting ................................ ................................ ................................ .......... 44 Study Participants ................................ ................................ ................................ ... 45 Confidentiality ................................ ................................ ................................ ......... 46

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6 Data Collection ................................ ................................ ................................ ....... 47 Measures ................................ ................................ ................................ ................ 48 Null Hypotheses ................................ ................................ ................................ ...... 50 Data Anal ysis ................................ ................................ ................................ .......... 50 4 RESULTS ................................ ................................ ................................ ............... 53 Description of Sample ................................ ................................ ............................. 53 Hypotheses Test and Results ................................ ................................ ................. 55 Hypothesis 1 ................................ ................................ ................................ ..... 55 Hypothesis 2 ................................ ................................ ................................ ..... 57 Hypothesis 3 ................................ ................................ ................................ ..... 57 Hypothesis 4 ................................ ................................ ................................ ..... 58 Hypothesis 5 ................................ ................................ ................................ ..... 58 Hypothesis 6 ................................ ................................ ................................ ..... 59 Change in Severity of Charges ................................ ................................ ......... 59 Summary of Findings ................................ ................................ .............................. 60 5 DISCUSSION ................................ ................................ ................................ ......... 73 Discussion of Findings ................................ ................................ ............................ 73 Hypothesis 1 ................................ ................................ ................................ ..... 75 Hypothesis 2 ................................ ................................ ................................ ..... 77 Hypothesis 3 ................................ ................................ ................................ ..... 78 Hypothesis 4 ................................ ................................ ................................ ..... 80 Hypothesis 5 ................................ ................................ ................................ ..... 80 Hypothesis 6 ................................ ................................ ................................ ..... 81 Conclusions ................................ ................................ ................................ ...... 81 Limitations of the Study ................................ ................................ ........................... 82 Implications ................................ ................................ ................................ ............. 85 Implications for Theory ................................ ................................ ..................... 87 Implications for Practice ................................ ................................ ................... 88 Summary ................................ ................................ ................................ ................ 89 APPENDIX A DATA COLLECTION FORM ................................ ................................ ................... 90 B FORENSIC DIVERSION TEAM INTAKE FORM ................................ .................... 92 C ................................ ...................... 96 D SEVERITY OF CHARGES CHART ................................ ................................ ........ 97 LIST OF REFERENCES ................................ ................................ ............................... 98 BIOGRAPHICAL SKETCH ................................ ................................ .......................... 104

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7 LIST O F TABLES Table page 4 1 Characteristics of sample population ................................ ................................ .. 62 4 2 Self reported and staff diagnosed diagnoses ................................ ..................... 63 4 3 Treatment and social support services ................................ ............................... 64 4 4 Criminal involvement variables ................................ ................................ ........... 65 4 5 Participant groups ................................ ................................ ............................... 66 4 6 Chi square test results by status group ................................ .............................. 68 4 7 Descriptive statistics for age and criminal involvement (CI) ................................ 66 4 8 Regression table for length in treatment and post diversion arrests ................... 70 4 9 Regression table for social support services an d post diversion arrests ............ 70

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8 LIST OF FIGURES Figure page 2 1 Sequential Intercept Model ................................ ................................ ................. 41 4 1 Comparison of pre diversion and post diversion rates between participants and non participants for A) arrests and B) jail days. ................................ ........... 71 4 2 Change in severity of charges, differentiated by statu s groups. ......................... 72

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9 LIST OF TERMS C OMPLETERS Participants in the Forensic Diversion program who successfully met all treatment objectives and maintained stability in the community for a least a period of 3 months C RIMINAL INVO LVEMENT Includes arrests and any days spent in jail C RISIS SERVICES Treatment services that are needed by an individual on an urgent basis, including crisis stabilization or detoxification D ISCHARGED Participants in the Forensic Diversion program who did not successfully complete the program and were discharged prior to meeting all treatment objectives F ORENSIC SPECIALIST Counselor assigned to a participant in the Forensic Diversion program who is responsible for case management and/or treatment duties I NPATIENT TREATMENT SERVICES Treatment services offered in an inpatient setting, including a 28 day residential substance abuse treatment program, or a dual diagnosis substance abuse treatment program last 6 to 9 months L ENGTH IN TREATMENT Time period par ticipants in the Forensic Diversion program were engaged in treatment, calculated as number of days between the date of acceptance and the date of discharge from the program M ANDATED TREATMENT Individual is court ordered by a judge to participate in treat ment for mental health and/or substance abuse issues N ON PARTICIPANTS Individuals who met acceptance criteria for the Forensic Diversion program, but personally decided not to engage in the program O UTPATIENT TREATMENT SERVICES Treatment services offered on an outpatient basis, including medication management, case management, psychosocial rehabilitation, and specialized mental health or substance abuse programs S OCIAL SUPPORT SERVICES Referrals or linkages to ancillary services including housing, employ ment, vocational services, educational services, and benefits coordination T REATMENT SERVICE UTILIZATION treatment services

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10 Abstract of Dissertation Presented to the Graduate School of t he University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy JAIL DIVERSION AND RECIDIVISM: IMPACT ON COMMUNITY INTEGRATION AND TREATMENT UTILIZATION By Shinlay Chu Rivera May 2013 Chair: Har ry Daniels Major: Marriage and Family Counseling The increasing number of inmates in jail and prison has prompted an examination of individuals in need of treatment for mental health illnesses and/or substance abuse issues. Diversion programs have demons trated effectiveness by reducing re arrests post diversion and linking individuals to treatment. This study utilized secondary data collected from a local jail diversion program. The purpose of this study was to investigate variables affecting recidivism including: diversion participation, treatment utilization, mandated treatment, length of treatment, linkages to social support services, and criminal history. Study participants were classified into three groups: program completers, discharged, and non p articipants The study hypotheses were tested using ANOVA, chi square, and regression analyses Study participants ( N =283) were referred and accepted to the diversion program between July 2008 and March 2011 The sample was predominantly males ( N =209, 73. 9%), and was represented by mostly African Americans and Caucasians Participants reported having mental health diagnoses and substance abuse issues. The three status groups were tested for pre diversion differences in age, gender, ethnicity, educational level, mental health diagnoses, substance abuse disorders,

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11 number of arrests, number of jail days, and severity of charges. Significant differences were found for ethnicity, diagnoses, pre diversion arrests, and pre diversion jail days. No differences w ere found in post diversion criminal involvement and treatment service utilization for court mandated/non mandated participants. Other findings indicated program participants had a higher average of post diversion arrests and jail days, as well as a highe r treatment utilization rate, than non participants. Significant relationships were found for length of time in treatment and linkage to social support services specifically housing, for post diversion arrests only, and not jail days or severity of charg es. This study also suggested a different interpretation of the severity of charge variable, calculated as change in severity. A discussion of the study s results, limitations of the st udy, and implications for future research, theory, and practice were presented.

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12 CHAPTER 1 INTRODUCTION The issue of overcrowding in the jail and prison systems has generated interest reversion of an individual to criminal behavior after he or she has been convicted of a used as a measure of correctional effectiveness and commonly used in evaluating correctional programs (Maltz, 2001). Recidivism can be exacerbated by many factors and one factor is mental illness. Hall, Miraglia, Lee, Chard Wierschem, and Sawyer (2012) noted research investigating predictors of recidivism were similar for both offenders in the general population and those with mental illnesses. The challenges faced by individuals with mental illness returning to the community are far more complex. Hall et al. (2012) studied a sample of 2,185 inmates with serious mental illness leaving the pri son systems in the state of New York between 2006 and 2007. In their study, Hall et al. (2012) defined serious mental when the individual was released from prison. The most common mental health diagnosis was Schizophrenia (29%), followed by Bipolar (26%), Psychosis (20%), and Major Depression (19%). In addition, about 60% of the sample had a diagnosis of substance abuse, and 16% had a diagnosis of antisocial personalit y disorder. Hall et al. found the factors of number of previous arrests, the diversity of the arrests, and the number of previous prison terms, as predictors of re arrest. Although none of the mental health diagnoses increased the chance of re arrest, a diagnosis of substance abuse increased re arrest chances by 26%. Study results demonstrated participating in

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13 treatment related to a minor but reduced chance (16%) of re arrest 30 days post release. Individuals participating in the Parole Supported Treatm ent Program (PSTP) at the time of release into the community, consisting of parole supervision, case management, treatment, and supported housing, also reduced the rates of re arrest by 107% (Hall, et al., 2012). It was unclear how long these effects cont inued, though the authors noted that the effects decreased over time, possibly due to individuals no longer being on parole. Further analysis indicated age, gender, and prior history of violence were found to be significant predictors of re arrest for vio lence. Jail and prison systems are not an ideal or appropriate setting to meet the treatment needs of incarcerated individuals with mental illnesses and/or substance popul ation as needing to be prioritized in the treatment system (p. 245). Lindqvist (2007) also noted the complex combination of offenders with psychiatric disorders and co t o sophisticated multi The purpose of this study was to examine the effectiveness of a jail diversion and successful integration in to the community Other variables explored were treatment service utilization, mandated versus non mandated treatment, length in treatment, and linkage to social support services. Statement of the Problem In the years following the deinstitutionalization movement, people with serious mental illness were transferred out of mental health institutions and into the community. There were not enough community based treatment services or support in place during

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14 this transfer, resulting in many people becoming ho meless or being arrested (Talley & Coleman, 1992). Many individuals remained untreated when they returned to the community. State mental hospitals treated primarily individuals committed from civil or criminal courts with few beds available for anyone el se (Bloom, 2010). Torrey, Kennard, Eslinger, Lamb, and Pavle (2010) found there was one psychiatric bed for every 300 Americans in 1955. By 2005, there was only one psychiatric bed for every 3,000 Americans. Advances using psychiatric medications had si gnificantly improved services for many people (Bloom, 2010); however many clients were not compliant with their medication routine when they were in the community (Talley & Coleman, 1992; Torrey et al., 2010). Access to treatment was hindered by the avail ability of insurance coverage and when available, benefits only covered acute care (Talley & Coleman, 1992). Some individuals were also discharged with limited social support, their families were unwilling or unable to take care of them, and this increase d homeles sness (Talley & Coleman, 1992). with mental illness ended up in the criminal justice system (Harcourt, 2006, p.1754). Criminalization With a lack of treatment options, many pe ople with mental illnesses ended up in the criminal justice system (Slate, 2009). Lamb, Weinberger, and Reston Parham severely Combined with the lack of basic resources, social support, and treatment, their behaviors led to public disturbances and the commitment of minor property offenses and sometimes more serious criminal acts (Hylton, 1995). Torrey et al. (2010), in taking 2005 state data on inmates in carcerated in jails and prisons, found seriously mentally ill people were three times more likely to end up in jails and prisons

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15 rather than hospitals, however treatment is not the primary function of the jail or prison systems. Beginning in the early 198 0s, the United States started undergoing what has result of increased number of mentally ill individuals being incarcerated in the jail and prison systems. At a time when deinstitutionalization was shifting individuals with mental illnesses from mental health institutions, a new shift occurred with these individuals entering the criminal institutions instead (Bloom, 2010). Jail and Prison Capacities Jail faciliti es are typically operated by local law enforcement agencies as holding facilities for people awaiting local arraignment, trial, conviction, or sentencing (Robst, Constantine, Andel, Boaz, & Howe, 2011; U.S. Department of Justice, 2011a). In addition to co nfinement, it has been estimated approximately 46% of all jails provide work release or pre release programs, 25% had a reception, diagnosis, or classification functions, and 10% had a drug and alcohol treatment component (U.S. Department of Justice, 2011a ). Recent trends have also indicated the number of facilities is decreasing while the number of inmates is increasing. Bureau of Justice Statistics found there were 3,283 jail facilities in the United States in 2006, a 3% decrease from 1999 (U.S. Departme nt of Justice, 2011a). There were a total of 762,003 inmates confined in jail, a 23% increase from 1999. During 2010, approximately 13,120,947 arrests were made by law enforcement and drug abuse violations accounted for the highest number of arrests (Fe deral Bureau of Investigation, 2011). More than 45% of the drug arrests were for possession of marijuana and 16.4% of the arrests were for possession of heroin, cocaine, or derivatives (Federal Bureau of Investigation, 2011). Between 2009 and 2010, the j ail

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16 population in county and city jails was 748,728 inmates (U.S. Department of Justice, 2011b). Slate (2009) estimated a jail population turns over about 20 times per year, indicating there may be a large number of re offenders. Once involved in the cri minal justice system, the process of the revolving door has been difficult to reverse. Individuals have been cycled from the system to the community and without adequate treatment they committed another offense bringing them back into the system again ( Steadman, Osher, Robbins, Case, & Samuels, 2009). Treatment Needs Approximately 56% of prison inmates in 2005 and 64% of jail inmates in 2002 had a mental health problem (Substance Abuse and Mental Health Services Administration, 2010) Serious mental il lness is prevalent in about 16% of surveyed prison inmates in 1997 and jail inmates in 1996 in the nation (U.S. Department of Justice, 2011a). Individuals in the study were identified as mentally ill either by their self report of a current mental or emot ional condition, or report of an overnight stay in a mental health hospital or treatment program. These rates were generally estimates, and it may not be possible to generalize the rates to all jails. Roesch, Ogloff, and Eaves (1995) advised each jail fa cility to establish its own formal assessment program for identifying the prevalence and needs of its inmate population with a mental illness. Broner, Mayrl, and Landsberg ( 2005 ) found 41% of the 175 jail inmates interviewed in a New York City jail were d iagno sed with Schizophrenia, 18% with Bipolar Disorder, 40% with Major Depression, and 51% had a polysubstance dependence diagnosis. About 65% of jail inmates with mental illness and 57% of other inmates were using drugs at the time of their arrest (U.S. Department of Justice, 2011a). It is important for treatment programs to address both of these issues concurrently. People with co occurring

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17 mental health and substance abuse disorders reported a greater need for services including housing, medical, family support, obtaining food and clothing legal assistance, vocational and employment assistance, and help with applying for public assistance (Broner, Lamon, Mayrl, & Karopkin, 2003). Broner et al. (2003) also noted even though this population ha d a higher utilization rate of the criminal just ice, health, emergency room, and shelters, only 8% reported a need for mental health treatment and 11% for substance abuse treatment. Jail and prison facilities are not equipped to serve as mental hospitals, as most s taff members are not trained to work with peo ple with a mental illness (Sung, Mellow, & Mahoney 2010; Torrey et al., 2010). Torrey et al. also stated several other problems in housing people with mental illnesses in jails or prisons. Mentally ill offend ers were expected to have a higher recidivism rate when compared to other released offenders. They were also expected to stay longer, due to difficulties understanding and following jail or prison rules, or are kept longer due to waiting for a more approp riate placement to become available. Mentally ill offenders were noted as contributors to management problems and found to be more likely to commit suicide in jails. They were also more likely to be abused by staff members since most have not been traine d to wor k with mentally ill offenders (Torrey et al., 2010). Another issue of concern is the escalating cost of housing inmates with mental illnesses. Slate (2009) reported it was twice as expensive housing individuals with mental illnesses in jails than to treat them in the community. In Broward county, Florida, cost was estimated at $80 per day for hous ing an inmate but $130 per day for housing an inmate with a mental illness (Torrey et al., 2010). Torrey et al. reported the

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18 increased costs wer e due to psychiatric evaluations, psychotropic medications, and increased liability. Treatment Utilization Treatment programs for offenders have demonstrated success in reducing recidivism (Olver, Stockdale, & Wormith, 2011). However, participants do not complete treatment for various reasons, including dropping out, not meeting treatment compliance, or re incarceration. McMurran and Theodosi (2007) found through a meta analysis of 16 different correctional program studies non completers of treatment had a higher recidivism risk comparable to untreated individuals. Olver et al. (2011) reviewed 114 offender treatment studies with information on 41, 438 offenders and calculated a treatment attrition rate of 27.1%. They also noted the lowest attrition rates were se en in cognitive behavioral treatment programs and prison based treatment programs. They found recidivism rates were 10 23% higher for treatment non completers in both general and violent recidivism. Treatment non completers were more likely to be young, single, unemployed, minority, had limited formal education, low income, and a prior history of offenses and incarcerations (Olver et al., 2011). Sung et al. (2010) found inmates with co occurring mental health and substance abuse issues represented abou t one third of the entire jail population using a nationally representative sample. An analysis of u tilization rates of substance abuse or mental health services in the jail found 35.7% received either substance abuse or mental health treatment services, 64.3% received neither, and only 6.1% received both. Sung et al. (2010) discussed factors contributing to this underutilization, including the transiency of the jail population and the limited budgets for treatment programs and training staff. In Broner study with 281 pre arraigned arrestees in Brooklyn, New York,

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19 31% reported some psychiatric diagnosis during their lifetime, and about 45% were likely to be dependent upon or abusing alcohol and/or drugs. About 43% of the sample reported s ome use of either substance abuse or psychiatric treatment services. Only 11% described a need for substance abuse treatment and 8% for mental health treatment. About 61% requested for some sort of social services, which included assistance with employme nt, housing, education, medical care, legal, family support, public, basics (food, clothing), healthcare for children, child care, and domestic violence. This may have attributed to the low number of requests for treatment services since the needs for soc ial services and basic needs were so high (Broner, et al., 2003). Theoretical Framework abuse, and criminal histories can be difficult for treatment providers especially as indiv iduals repeatedly cycle in and out of systems I t is often impossible to understand therefore it is helpful to have some insight into what perpetuates this cycle. Rott er, (p. 265) as a way of life developed by individuals with a history of incarceration. These behaviors 5) and serve as skills needed for survival while the individual is in a jail or prison setting. Rotter et al noted that these behaviors can directly interfere with what is expected in treatment vation for treatment, In a review of the literature on recidivism and diversion, there were a few theories and models available to guide research in this area. The following theories were selected

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20 for this study as a possible basis for any causal or correlational relationships among the factors. Self Control Theory (1990) self control theory. The basis of the theory rest ed on the assumption that people with low self control have a series of characteristics, including impulsivity, insensitivity, orientation to action, risk taking, short sightedness, being non verbal, and unable to meet the respo nsibilities of school, work, and family. They also have an increased 2000, p. 932). G ottfredson and Hirschi (1990) reported the development of self control is expected to develop in childhood before the age of 10. They theorized parents help the child develop self control by being able to monitor, recognize, and redirect the child from de viant behaviors. Research applying the self control theory to criminal justice system outcomes found low self control as a predictor of crime (Pratt & Cullen, 2000). After completing a meta analysis of 21 existing studies using 17 independent data sets, P ratt and Cullen (2000) found when compared with other predictors of crime, low self control was one of the strongest predictors of crime. In completing a 5 year longitudinal study with 601 graduates of boot camp program for male offenders, Benda (2003) al so found low self control to be a strong predictor of recidivism. Applying self control theory, an individual with low self control would be expected to have difficulties in being compliant with both the expectations of a treatment program and the crim inal justice system. DeLisi and Berg (2006) described criminal sanctions as

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21 Self control theory would be helpful in look ing at program development or evaluation. Self control theory focuses on individual factors contributing to the lack of success in a program. If the program participants can be assessed early in a treatment program for their level of self control, indivi duals needing additional specialized treatment can be identified. Other additional services could include cognitive behavioral and social skills interventions, which have been found to improve program effectiveness rather than standard behavior modificati on programs (Pearson, Lipson, Cleland, & Yee, 2002). It is important for program components to work on treatment program. Therapeutic Alliance and Readiness Investigatin g diversion programs from only a program evaluation standpoint is not enough. A commentary by Carr (2012) in response to the study mentioned earlier by Hall et al. (2012) acknowledged the role of mental health interventions in reducing recidivism but fa iled to address the significance of the therapeutic relationship between the client and treatment provider on chara cteristics of individuals and not the environments in which they live is invariably to focus solely on treatment, but also take into account how the therapeutic relat ionship s could be strengthened and connections made for the individual to family and social supports.

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22 Therapeutic a lliance. The concept of the therapeutic alliance is grounded in conceptualized it into a pan theoretical model of the working alliance including three factors: goals, tasks, and a change and the one who offers to be a change agent is one of the keys, if not the key, Another factor often assessed in offender rehabilitation research is treatment readiness. Ward, Day, Howells, and Birgden (2003) defined this readiness as a person ), is able to respond appropriately (i.e., perceives he or she can), finds it relevant and meaningful (i.e., can engage), and has their meta analysis with offender tr eatment studies, Olver et al. (2011) found higher levels of motivation and treatment engagement predicted lower treatment attrition rates. Ward et al. (2003) noted treatment readiness is similarly related to the concepts of motivation and responsivity. treatment and willingness to change behavior. Responsivity was defined as a modality of treatment or intervention that maximizes learning. Ward and colleagues (2003) argued that the readiness factor i s more positively focused and more applicable to various contexts as compared to motivation or responsivity. Treatment readiness The stages of change as outlined in Prochaska and has been used to demonstr ate how TTM divides the change process into 5 steps, each containing specific tasks to achieve and sustain changes. The stages are:

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23 1) Pre contemplation, 2) Contemplation, 3) Preparation, 4) Action, and 5) Mainte nance. stage of readiness for change. An important point about this model is the spiral pattern as opposed to a linear pattern of progression through the stages (Prochask a, DiClemente, & Norcross, 1992). This fact can be used to explain the return of individuals to past behaviors, whether it is a relapse in mental health or substance abuse issues, or a return to criminal behaviors. A brief description of each stage is li sted below: Precontemplation: Individual is not ready to change. Contemplation: Individual is undecided about change, but thinking about it. Preparation: Individual is now ready for change. Action: Individual is actively making changes. Maintenance : Acti vely maintaining changes and preventing relapse. The d iversion program used in this study incorporated phases of treatment closely following the stages of change. The phases of the program were identified as the following: Phase I (Pre engagement): Inclu ded p re treatment services, such as motivational enhancement and interviewing, program orientation and education, and linkages community resources. Phase II (Recovery Services): Active t reatment services including: individual/group therapy, case management medication management, and drug testing. Phase III (Community Transition): Represented advanced level of self sufficiency, client continues in individual and group therapy, case management, medication management, drug testing, and increased usage of peer support services. Phase IV (Aftercare): Recommended for clients requiring minimal services to maintain stability, continued participation in group therapy, drug testing, and predominant use of peer support services.

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24 While the research questions for this s tudy did not address treatment readiness directly, this information is perceived to be helpful in explaining the results of the study. Need for the Study Previous research with diversion program generally focused on one specific diversion model, the assess ment of a few outcome measures, one jurisdiction, or one point in the crim inal justice process (Broner et al 2004) Steadman (1999) stated there is still a need to identify which types of programs work best for certain groups of people, or for certain c ommunities. This may be a reason for mixed research results, as the same diversion programs are applied to different groups of people with different needs (Roesch et al 1995). Butzin, Saum, and Scarpitti (2002) proposed that more information on individ ual client factors can be used to determine what clients are likely to be more engaged or more likely to drop out of a treatment program. Steadman and Naples (2005) in a study of six jail diversion programs found the results confirmed previously published studies on jail diversion in terms of the effectiveness of diversion. Diversion participants had approximately 58 more days in the community and fewer arrests during a 12 month follow up period when compared to non participants. Steadman and Naples (200 5) indicated diversion program research is a developing area of study and directed future studies to specify what specific treatment services are rendered. They also noted few diversion programs offer sufficient access to Assertive Community Treatment (AC T), psychotropic medications, and integrated treatment programs. This mix of services was found to be ideal in the treatment of individuals with serious mental illnesses and/or co occurring substance use issues (Steadman & Naples, 2005). Roesch et al. ( 1995) also recommended careful

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25 record keeping and information sharing were needed between the criminal justice and mental health systems to account for the people cycling through these two systems. The current study took into consideration th e needs listed above. The diversion program examined for this study utilized an integrated approach including: treatment, medication management, and wrap around services pertaining to case management, housing, benefits coordination, and linkage to employm ent, educational, and vocational resources. The study also examine d multiple variables congruently as well as different types of mental health and substance abuse issues. The findings from this proposed study will help treatment providers, program devel opers, and grant funders gain a better understanding of how to maximize the outcomes of these programs. This study will also offer clinicians new perspectives when responding to treatment barriers In this population, most individuals have a history of i ncarceration, and can include an overlap of complex issues pertaining to criminality, psychiatric issues, addictions, and social service needs. By examining multiple factors, clinicians will hopefully be able to improve decision making in clinical practic e by creating a more holistic approach. Purpose of the study The purpose of the study wa s to investigate the effectiveness of jail diversion programs in reducing recidivism in the criminal justice system for individuals with mental health and/or substanc e abuse issues and their successful integration into the community The study investigate d multiple components at the same time, related to variables affecting recidivism, retention in diversion programs, the long terms effects relating to recidivism in t choice as a motivator for treatment, and the effects of treatment duration and linked

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26 social services. Static variables include d demographic information such as age, gender, ethnicity, and educational level, as well as criminal history, including number of arrests, number of jail days, and severity of charges. Dynamic and clinical factors were also included, such as employment status, mental health diagnoses, substance use, and treatment u tilization. Andrew s Bonta, and Hoge (1990) noted the importance of including both types of factors, as dynamic factors reflect what is amenable to change and addressed in treatment. The following questions address these additional components: Research Questions This study used secondary data collected from a jail diversion program which utilized treatment services from a local community mental health agency. The participants in the program were categorized into 3 groups, classified as completers, disc harged and non participants Post diversion information will also be used to determine the impact of participation in a diversion program. RQ1: Are there differences between the completers, discharged, and non participants in predisposing factors (age, g ender, ethnicity, educational level, employment status, diagnoses) and criminal history (number of arrests 1 year pre diversion, number of jail days 1 year pre diversion, severity of charges 1 year pre diversion)? RQ2: Are there differences between manda ted and non mandated participants in post diversion criminal involvement and treatment service utilization using age as a covariate? RQ3: Does diversion participation have a significant impact on criminal involvement (number of arrests 1 year post divers ion, number of jail days 1 year post diversion, severity of charges 1 year post diversion) between participants (completers and discharged) and non participants? RQ4: Does diversion participation have a significant impact on treatment service utilization between participants (completers and discharged) and non participants?

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27 RQ5: Does length of time in treatment have a significant impact on criminal involvement (number of arrests 1 year post diversion, number of jail days 1 year post diversion, severity o f charges 1 year post diversion)? RQ6: Do housing, employment, vocational services, educational services, and benefits coordination predict number of arrests, number of jail days and severity of charges?

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28 CHAPTER 2 LITERATURE REVIEW According to the World Health Organization (2011), mental health is defined as being in which every individual realizes his or her own potential, can cope with the normal stresses of life, can work productively and fruitfully, and is able to make a contribution disorders can cause distress and impairment when experienced separately, but co occurring issues can be challenging for both treatment and recovery ( Substance Abuse and Mental Health Services A dministration, 2010). In the general population, having co occurring disorders can contribute to the risk of homelessness, poor treatment compliance, suicidal behavior, hospitalization, infectious dise ases, and violence ( Lattimore, 2003). Individuals wi th serious mental illnesses also have an increased likelihood of arrests and incarcerations, though the reason for this phenomenon is unclear (Junginger, Claypoole, Laygo, and Crisanti, 2006). In their study, Junginger et al. (2006) found among 113 partic ipants at least 23% of the offenses were directly or indirectly related to substance abuse, making substance abuse a more significant causal factor for criminal behavior than serious mental illness. It is not possible to simply group people into categorie and Bonta (2006) identified eight central factors that predict crime, which are the same for individuals with or without mental illnesses. The eight factors include antisocial behaviors, associates, attitudes, personality patterns, poor school and work performance, estrangement from family, substance use disorders, and a lack of prosocial recreational pursuits. Most of incarcerated individuals with mental illness have

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29 complex histories which could in clude criminality, psychiatric issues, additions, and social service needs (Lurigio, 2012). It is expected that this could make interventions and treatment options difficult to apply in a generalizing manner. This chapter will review information to better understand the treatment needs and barriers to treatment for individuals in the criminal justice system who have a mental health and co occurring substance abuse disorder. Information will also cover diversion programs, created to divert these individual s to more appropriate programs based on these needs. Studies outlining the benefits of these diversion programs will be presented, leading to the description and goals of the local diversion program examined for this study. Mental Illness in the Jail a nd Prison Systems Lurigio and Lewis (as cited in Lurigio, 2012) suggested three groupings for people with serious mental illness in the criminal justice system. The first group mptoms were disrupting the social order (Bittner, 1967; Lurigio, 2012). The second group sales. The third group included those who were arrested for serious crimes such as robbery, burglary, or homicide. (Robst et al., 2011; U.S. Department of Justice, 2011a) A study on the treatment of seriously mentally ill individuals in jails throughout the nation show ed that 29% of the jails surveyed held seriously mentally ill individuals without any criminal charges (Torrey et al. 1992). For these individuals, the jail becomes a holding place while they wait for a psychiatric

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30 evaluation, for a psychiatric bed to be come available, or for transportation to a psychiatric hospital (Torrey, 1995). Specialized treatment services for inmates with severe mental illnesses are often not available in the jails. Torrey and colleagues found in their 1992 survey more than one out of every 14 (72%) inmates has a serious mental illness. Their survey also showed among 41% (1,391 jails) of all jails in the United States, more than one in five jails had no access to mental health services at all. In relation to training for staff, 84% of the jails surveyed reported that their officers rec eived no training or less than three hours of specialized training on issues experienced by this population. Diversion Programs Diversion programs have been developed as a way to address the needs of individuals with mental illness in the criminal justice system. Jail diversion pr ograms were started in the 1960 s due to the high number of minor offenses processed in the clogged court system (Roesch et al., 1995). The jail diversion model utilized the same rationale for community support programs which aligned with the deinstitutionalization movement and prioritized treatment in a less restrictive environment (Draine & Solomon, 2005). For many individuals, jails were also the entry point into the b ehavioral healthcare system (Sung et al., 2010) beginning with their first diagnosis of a mental disorder (Hylton, 1995). Diversion programs have been defined by Steadman and colleagues (1999) as: s pecific programs that screen detainees in contact with the criminal justice system for the presence of a mental disorder; they employ mental health professionals to evaluate the detainees and negotiate with prosecutors, defense attorneys, community based mental health providers, and the courts to develop commu nity based mental health dispositions for mentally ill detainees. The mental health disposition is sought as an alternative to prosecution, as a condition of a reduction in charges, or as satisfaction for the charges, for example, as a condition of probat ion. Once such a disposition is

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31 decided on, the diversion program links the client to community based mental health services. (p.1620) process, will increase access to community treatment, housing, and adjunctive services, avoid or shorten criminal justice confinement, and, by linkage into treatment, will reduce substance use, psychiatric symptoms, and criminal justice recidivism, and increase (Broner, Lattimore, Cowell, & Schlenger, 2004, p. 521) Diversion efforts can either prevent incarceration or reduce the length of incarceration (Draine, & Solomon, 2005). Rogers and Bagby (as cited in Steadman, Morris, & Dennis, 1995) stated may be the only viable alternative to the rapid cycling of Diversion programs have been differentiated based on the point of when the individual is transferred from the cr iminal justice system to the mental health system (Lamb et al., 2006). Pre booking diversion occurs when the individual is diverted at initial contact with law enforcement prior to formal charges being filed (Steadman et al., 2005) L aw enforcement off icers can determine the necessity of the arrest at this point and divert the individual to a treatment facility if appropriate (Lattimore et al., 2003). An example of a pre booking diversion program is the use of Crisis Intervention Teams, which are speci ally trained groups of law enforcement officers who are able to deal with people in mental health crises, diverting the clients from the criminal justice system and linking them to appropriate treatment services in the community (Slate, 2009). Post bookin g diversion occurs when the individual is diverted at some point after their arrest or booking into the jail, and can be either court or jail based diversion (Broner et al., 2003; Steadman, Morris, & Dennis, 1995). Steadman and colleagues (1995) reported

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32 that the following six factors have been found in the most effective jail diversion programs: 1) integrated services, 2) regular meetings of key agency representatives, 3) boundary spanners, 4) strong leadership, 5) early identification, and 6) distinctive case management services. According to Steadman et al. programs are those that are part of a comprehensive array of other jail services, including screening, evaluation, short term treatment, and discharge planning, that ar e integrated with community based mental health, substance abuse, and housing based diversion can take the form of specialized mental cha rges are dismissed after completing treatment for a specific period of time (Lattimore judge, who links the person to treatment through a specialized team comprised of a designated prosecutor, defense attorney, and treatment providers. This supervision and oversight by the court and treatment providers can be seen as a form of coercion, as participation in treatment is encouraged in order to reduce negative behaviors (L attimore et al., 2003). Lattimore et al. (2003) suggested the differences in programs are not just about the point of diversion, but possibly targeting different groups of people altogether. In their study with 1,966 subjects, differences between divert ed individuals who participated in pre booking and post booking programs were examined. Individuals considered for post booking programs have been more functionally impaired than those in pre booking programs, with a more complex mental health background, heavier drug and alcohol use, and more arrests. Pre booking program participants had fewer

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33 arrests, less serious offenses, and less likely to have multiple arrests over the past year. They also reported higher education levels, employment, and satisfact ions with their lives, health, and finances. Though there was not a significant difference in reported mental health issues, post booking program participants reported more drug and alcohol use. Post booking program participants were more likely to util ize mental health or substance abuse treatment three months prior to contact with the police. General Characteristics of Diversion Participants Some general characteristics have been identified among those individuals who decided to participate in diver sion programs and those who did not. Diverted individuals were more likely to have a diagnosis of schizophrenia or mood disorder with psychotic features (Broner et al., 2004; Steadman & Naples, 2005). They were also more likely to be female and receive S SI/SSDI, and were less likely to live with a spouse or partner (Steadman & Naples, 2005). Diverted individuals reported more days in the community, and in relation to treatment, were more likely to have been hospitalized, gone to the emergency room, or re ceived medications (Broner et al., 2004). There was not a difference between the diverted and non diverted in terms of mental health and substance abuse counseling involvement prior to engagement in a program (Broner et al., 2004). Non diverted individuals were more substance involved (Broner et al., 2004; Steadman & Naples, 2005). They also had more prior arrests (Steadman & Naples, 2005) and reported a younger age at first arrest (Broner et al., 2004). These measures have been identified as predictors o f future criminal behaviors (Broner et al., 2004). Diverted individuals were more likely to have a violent charge, and spent more days in jail 12 months prior to treatment engagement (Broner et al., 2004).

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34 Effectiveness of Jail Diversion Programs The re is a limited number of published empirical studies regarding the outcomes of diversion (Steadman & Naples, 2005), and the results are mixed (Roesch et al., 1995; Broner et al., 2004). Poor outcomes have been associated with psychiatric hospitalizations arrests, significant physical violence against persons, or homelessness during the one year period post diversion (Lamb et al., 1996). Case, Steadman, Dupuis, and Morris (2009) noted that initial research included mostly descriptive studies, followed by decision making and fiscal impact of jail diversion programs, and concluded that though prior research has demonstrated the effectiveness of jail diversion programs in reducing jail days, it has not been clear how the achievement of these outcomes are rel ated to services used. Despite the known benefits of diversion programs, individuals who are high risk and have the most needs, would benefit from these programs the most, but are often the least likely to complete them (Olver, Stockdale, & Wormith, 2011) Results from their meta analysis with offender treatment studies showed treatment non completers were more likely to be young, single, unemployed, an ethnic minority male, with limited education, with low income, and had a history of prior offenses and incarceration. Less criminal involvement Recidivism to the criminal justice system has been measured by the number of re arrests during specified time periods post treatment. The number of re arrests has also been used as an indicator for community risk (Wormith & Olver, 2002). It has not been found that there is an increased risk for arrest for those who participate in diversion (Broner et al., 2004; Broner et al., 2005). Individuals participating in diversion were found to have more days in the commun ity (versus hospitalization and incarceration) than those who did not participate

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35 (Broner et al., 2004). Steadman et al. (2005) surveyed diverted and non diverted individuals in three pre booking and three post booking programs, where the diverted group r esulted in more days in the community and fewer re arrests over a 12 month follow up period. With the 546 participants interviewed by Case et al. (2009), 47.3% experienced no arrests after enrolling in a post booking diversion program. They found that prior arrests and prior jail days to be significant differences between those who experienced post status was also found to be a significant predictor of post diversion arrests (Case e t al., 2009). Less time in jail. Diverted individuals also spent less time in jail. Case and colleagues (2009) found in their sample with 546 participants that approximately 75% of the sample experienced a decrease in jail days post diversion. This resu lt has been further applied to those in mandated treatment, who had a higher amount of days in the community than those in non mandated treatment, and also created linkages to and increased time spent in treatment (Broner et al., 2005). Mandated participa nts were also found to have better outcomes than non mandated participants (Lamb et al., 1996), defined as the absence of any psychiatric hospitalizations, arrests, violence against persons, and homelessness. Post Diversion Treatment Utilization Steadman a nd Naples (2005) confirmed in their study that diverted individuals were more likely to report participation in counseling, hospitalizations, taking prescribed medications, and emergency room visits. However, Broner et al. (2004) found that even though di version increased the use of these mental health treatment services, especially in the 3 month follow up period, the difference did not sustain over time when

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36 examined at 1 2 months post diversion. The utilization rates for substance abuse treatment was even more stagg ering at 12 months post diversion, with only 0.7% of diverted individuals reporting going to two or more substance abuse counseling sessions. Broner et al. (2005) further delineated that recidivism effects are based on the types of treatment servi ces received. In their study, more time in residential treatment contributed to lower re arrest rates and fewer days in jail. This was also true for those who participated in outpatient treatment services. They also found medication compliance to be a p redictor of time spent in jail, with less compliance relating to more days spent in jail. Different Programs and Initiatives In 2007, the Florida Legislature passed House Bill 1477, creating the Criminal Justice, Mental Health and Substance Abuse Reinves tment Grant program (Florida Partners in Crisis, 2010). Thirty two counties in Florida have utilized this state funding, matched by the individual counties, to create programs to divert individuals with mental illness from jails, prisons, and state forens ic hospitals. Alachua county was designated as an implementation county in 2007, and then as an expansion grantee county in 2010. The Forensic Diversion Program that was created through this grant was designed to serve adults with 2 or more jail stays, a history of mental illness, substance abuse, or co occurring disorders, and a pending criminal case in Alachua county (The Criminal Justice, Mental Health, and Substance Abuse Technical Assistance Center, 2012). Forensic Diversion Program The Forensic Diversion Program was designed to be a clinical home for the participants accepted for diversion. The program was comprised of a team leader,

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37 three forensic specialists, two peer specialists, and a psychiatrist A jail diversion specialist was stationed at t he local jail and actively identified and assessed individuals who met program criteria. The county also provided staff in court services, jail classification, and pretrial release. The program use d the conceptual framework of the Sequential Inter cept Model to look at how the criminal justice and mental health systems overlap (Munetz & Griffin, 2006). This model propose d different points where an intervention can be applied to prevent the individual from progressing further into the criminal justi ce system, and divert them directly into treatment options (Figure 2 1) Ideally, these individuals are intercepted early before they fall deeper into the criminal justice funnel. Intensive mental health and substance abuse treatment services were p rovided to participants in the program, which consisted of four phases. Phase 1 is the pre engagement phase where accepted individuals were prepared for participation in the program. Most of these individuals were either still incarcerated or recently released from jail. Individuals in Phase 2 were actively engag ed in recovery services in an outpatient setting. Services comprised of individual and/or group therapy, case management, medication management, and drug testing to assess for continued sobriety. If needed, the participant was referred to more intensive levels of care, including crisis services at the crisis stabilization or detox units, or residential programs. Participants were also encouraged to use peer support services such as AA and NA grou ps. Phase 3 is the community transition phase, where the participants were able to demonstrate an advanced level of self sufficiency, and were referred to social support

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38 services i nclude d referrals to help with employment, continued education, establishment of social security benefits, vocational assistance, and housing. A participant wa s considered in aftercare services when in Phase 4, when the participant require d m inimal services to maintain the ir stability. The participant wa s not required to move through the phases in a linear fashion, as the assessme nt for needed services continue d throughout the program. The participant could potentially cycle through the phase s again if they relapsed with their substance abuse or cycle d through the criminal system. A participant successfully complete d the program when they completed their identified individual treatment plan and maintained at least a 3 month period of stabi lity in the community. Goals of the Forensic Diversion Program The program identified the following four goals for this cycle of the grants: 1) ensuring public safety, 2) increased connections to housing employment, and/or educational resources, 3) avoidi ng increased spending on criminal justice and forensic beds, and 4) increasing communication, collaboration, and partnership. Public safety wa s evidenced by a reduction in recidivism rates. Jail and court staff provided criminal history data at the point the participant was accepted by the offense prior to participation in the program. The same data was collected for 1 year pre diversion and 1 year post diversion. It w as expected that there would be reduction in arrests, jail days, and the severity of arrests. Diversion from the jail and linkages to treatment services were provided by increasing connections to housing, employment, and/or educational resources. These resources were identified as critical to re integration in the community and recovery. It

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39 was expected that the program would improve the homelessness rate by helping participants find stable housing. It was also expected that the program would link the participa nt to appropriate mental health and/or substance abuse treatment services and other needed social support services prior to discharge from the program. The program hoped to avoid increased spending on criminal justice and forensic beds. The goal was to provide appropriate care in the community, versus the person constantly cycling through the local jail or state hospital systems. Communication, collaboration, and partnership were seen as important components of success with the program. The goal was to increase the communication and collaboration among the different systems, including the jail, court, treatment, and community services. This was achieved through monthly meetings attended by the treatment providers as well as representatives from the var ious community agencies involved as referral sources. Summary This chapter reviewed literature on diversion programs, which have been utilized to alleviate the growing issue of persons with mental illnesses and co occurring substance abuse disorders bein g incarcerated. Though research results have been mixed, this intervention has shown to reduce recidivism to the criminal justice system through decreased number of arrests and number of days in jail post diversion. This study examined multiple variable s that contribute d to the successful completion of a community based jail diversion program, and examine d if participation in the program reduced recidivism to the criminal justice system Treatment service

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40 utilization was also examined, based on levels and types of services. The study also looked at the effects of mandated treatment, length of treatment, and the linkage to social support services such as housing, employment, vocational, educational, and benefits coordination as predictor variables for post diversion criminal involvement.

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41 Figure 2 Behavioral Health and Justice Transformation. 2011. Brochure.]

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42 CHAPTER 3 METHODOLOGY Diversion programs are increasingly being utilized for individual s who may benefit from a community based treatment program rather than continued incarceration due to the number of individuals already in local jails, state, and national prison systems. Diversion program interventions provide mental health and substance abuse treatment, can reduce the length of incarceration (Draine & Solomon, 2005), and prevent the rapid cycling of individuals with mental health or substance abuse treatment needs through the criminal justice system (Rogers & Bagby, 1992). The purpose o f this study was to examine the factors affecting success and retention in a community based diversion program and whether or not participation in a diversion program influenced recidivism to the criminal justice system and treatment service utilization. The study investigated the effects of court mandated versus non mandated treatment. The study accessed secondary archival data on individuals referred to and accepted for the Forensic Diversion Program in Alachua County, Florida. This chapter will address the research method, population and sampling, confidentiality, instruments or variables to be collected, data collection procedure, and data analysis. Six research questions posed for this study are as follows: RQ1: Are there differences between the comp leters, discharged, and non participants in predisposing factors (age, gender, ethnicity, educational level, employment status, diagnoses) and criminal history (number of arrests 1 year pre diversion, number of jail days 1 year pre diversion, severity of c harges 1 year pre diversion)? RQ2: Are there differences between mandated and non mandated participants in post diversion criminal involvement and treatment service utilization using age as a covariate? RQ3: Does diversion participation have a significan t impact on criminal involvement (number of arrests 1 year post diversion, number of jail days 1 year post diversion,

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43 severity of charges 1 year post diversion) between participants (completers and discharged) and non participants? RQ4: Does diversion part icipation have a significant impact on treatment service utilization between participants (completers and discharged) and non participants? RQ5: Does length of time in treatment have a significant impact on criminal involvement (number of arrests 1 year p ost diversion, number of jail days 1 year post diversion, severity of charges 1 year post diversion)? RQ6: Do housing, employment, vocational services, educational services, and benefits coordination predict number of arrests, number of jail days and sever ity of charges? Research Method A quantitative paradigm was selected over qualitative and mixed method for two reasons: appropriateness and need. In attempting to determine if there are differences between two or more variables or establishing a relation ship among variables using numerical data, a quantitative method is an appropriate selection over qualitative or mixed method (Gall, Gall, & Borg, 2006). Ary, Jacobs, Sorenson, and Razavieh (2009) identified a quantitative methodology as appropriate and usefu l when working with larger populations, to summarize the data through statistical analyses, and to determine whether to reject or fail to reject the null hypotheses. A quantitative methodology will enable comparisons of different groups of individuals and establishing whether or not there are relationships among variables. A quantitative research method was selected for this study to meet the needs of the study. When attempting to establish whether there is a relationship or a difference between two or mor e variables using numerical data, a quantitative method is an appropriate choice over qualitative or mixed methods (Cooper & Schindler, 2008). Cooper and Schindler identified a quantitative methodology as being beneficial when working with larger samples, for removing potential researcher bias, and applying the

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44 results to general populations. A quantitative approach was deemed as the most appropriate method to examine all different factors in this study. Study Setting The Forensic Diversion Prog ram in Alachua County was started in July 2008 to recidivism (more than two arrests) and who have symptoms or history consistent with a severe and persistent mental illn ess and a likely co (Criminal Justice, Mental Health and Substance Abuse Reinvestment Grant, 2008, p. B 5). Services provided through the program include d treatment, intensive case management, benefits coordination, vocati onal services, housing, and systematic collaboration with the criminal justice system. The project was funded by a state grant with matching funds from the county in the total amount of $1,998,000 over a period of 3 years. The Forensic Diversion Program was based on the Sequential Intercept Model (SIM) (Munetz & Griffin, 2006). SIM incorporate various points in time when individuals can be referred to the program. The SIM beg an with the premise that individuals with mental disorders should not be invo lved with the criminal justice system any more often than individuals without mental disorders. If individuals with mental disorders do commit crimes unrelated to their mental disorder, they should not be incarcerated simply because of their disorder or l ack of access to treatment. Munetz and Griffin proposed this as an ideal situation ; however, reality is different and they envisioned a system providing points of entr y into treatment rather than venturing further into the criminal justice system. Five points o f entry into the model were as follows: 1) Law enforcement and emergency services, 2) initial detention and initial hearings, 3) jail, courts, forensic

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45 evaluations and forensic commitments, 4) reentry from jails, state prisons, and forensic hospitalization ; and 5) community corrections and community support services. SIM require d an arr ay of services including: counseling community support services, case management, medications, vocational, safe and affordable housing, and crisis services (Munetz & Griffi n, 2006). Participants in the Forensic treatment group attend ed group therapy on an outpatient basis 2 days a week, for 3 hours a day. Other outpatient services offered at the agency include d medication management, case management, and specialized mental health or substance abuse programs. Inpatient treatment programs were comprised of the 28 day substance abuse residential tr eatment program at the agency, or longer for dual diagnosis clients, and another local residential treatment program with a 6 9 m onth treatment period. Crisis services were offered through the detox or crisis stabilization units. Study Participants Alachua County is located in North Central Florida with a population of 249,365 (U.S. Census Bureau, 2012) and covers an area of 875 square miles. The U. S. Census Bureau (2012) estimated the county was made up of Caucasians (71.3%), African Americans (20.3%), Hispanics/Latinos (8.9%), Asians (5.6%), American Indians and Alaska Natives (0.3% ) Native Hawaiian and Other Pacific Islander s (0.1%), and employed (56.7%) and had completed a high school education or higher (89.7%). The Florida Coalition for the Homeless (20 10 ) estimated the homeless populat ion in Alachua County was approximately 1,596 individuals. Criteria for participation in this study include d : being over the age o f 18, having two or more jail stays, and demonstrate d having a severe and persistent mental health

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46 and/or substance abuse dia gnosis. The majority of the individuals participating in this study were referred after they have been arrested and booked into the local jail, or post booking. An intake evaluation was completed for all individuals meeting the program criteria These e valuations were c ompleted while the individual was incarcerated or shortly after his/her release by a jail diversion specialist or a jail classification officer The intake evaluation capture d self reported information including demographic i nformation psychosocial information, mental health and/or substance abuse history, and current treatment services. The evaluator then ma de a treatment recommendation based on the intake data. Referrals to ancillary programs were also made at this point, including benefits coordination, vocational services, and housing. A forensic specialist from the Forensic Diversion Program was assigned to the individual and responsible for intensive case management, direct treatment services, referrals to appropriate services, and collaboration with criminal justice agencies. Additionally, participants had to be accepted by the state attorney and not have any current violent or sexual charges on their record. The sample used for this study consisted of individuals re ferred and accepted to the Forensic Diversion Program between July 1, 2008 and March 27, 2011. Program participants were expected to remain in treatment up to 120 180 days, with linkage s to treatment services and resources thereby reducing their criminal justice involvement post treatment (Criminal Justice, Mental Health and Substance Abuse Reinvestment Grant, 2008). Confidentiality Every research participant in any study has a right to privacy and expects any data will be kept confidential at all times. There is an expectation of respect for the autonomy, trust, scientific integrity, and fidelity in the study. Every research participant

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47 has the right to expect there will be no chance of being identified by name at any time, before, during, or after the study. While no data was collected in person from any individuals who participated in the diversion program, their records were accessed via treatment records, court information, and program databases. Due to having access to separate databases to obtain the necessary information, the researcher assigned each participant a randomly selected number using a table of random numbers. The list matching names and numbers was kept at the treatment agency as an encrypted computer file The encrypted file was sa network system, and will three years, or as otherwise determined by the treatment agency. Data collection forms (Appendix A) contained no personally ident ifying information and data was reported only in an aggregated format. Creswell (2003) suggested the fundamental role for ethical research is to do no harm, including physical, psychological, social, economic, or legal harm. No informed consent was neede d from participants as secondary data was used for the study; however, consent to access the data was granted by t he treatment agency Data Collection Data collection began u pon receipt of approval from the Institutional Review Board of the University of Florida. This study compile d data obtained from treatment records and criminal history information of individuals referred to the Forensic Diversion Program at a local community mental health agency in Alachua County, Florida between July 1, 2008 and Marc h 27, 2011. Individual records were accessed for each individual including information from three major sources. The first was their intake form ( Appendix B ) completed by the jail diversion specialist or jail classification officer

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48 when the individual was initially incarcerated or recently released from incarceration to capture s elf reported information relating to symptoms, diagnoses, treatment history, treatment chart maintained by the community mental health agency, including their treatment services and diagnoses. This informat implemented since 2007. The third source of information was the c riminal history database with information provided to the treatment agency by the local jail and judicial circuit court ( Appendix C ). Names were matched to randomly selected identification numbers; however identification numbers were only used on data collection forms (Appendix A). Measures T he data for this study was extracted from program databases. The study use d arc records. Each variable discussed below was collected from the databases for every individual meeting the criteria for participation in the study. Program participants were c ategorized as completers (1) discharged (2), or non participants (3). The completers and discharged groups included individuals who completed the diversion program or actively engaged for a period of time in the program. The non participants were indivi duals meeting acceptance criteria for the program but did not engage in program treatment. Age was collected as a year of birth and the current age calculated by subtracting birth year from the current year. Gender was collected as male (0) and Female (1 ). Program participants were asked their completed level of education at intake including: less that high school (1), high school graduate/GED (2), some college (3), 2 year degree (4), 4 Year degree (5), and graduate

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49 degree (6). Ethnicity was also collec ted from program records as: Caucasian (1), African American (2), Hispanic (3), Native American (4), Asian/Pacific Islander (5), Mixed Ethnic (6). A diagnosis for substance abuse or mental health problems was self report ed by each person at admission. Th e treatment diagnosis given by treatment staff was also recorded. Criminal history was recorded as a continuous variable of the number of arrests in the calendar year prior to admission into the diversion program. Number of jail days was collected as the number of days spent in jail in the calendar year prior to admission into the diversion program. Severity of charges was measured in the program database as a four point scale ranging from lowest severity (1) to greatest severity (4) (See Appendix D for a complete listing of charges). Greatest severity include d charges such as: murder, kidnapping, sodomy, and carjacking. Lowest severity include d charges such as: bigamy/polygamy, child neglect, disorderly conduct, and larceny. Moderate severity crimes i nclude d counterfeiting, child abuse, and hit and run while high severity included attempted arson, attempted kidnapping, aggravated battery, and aggravated child abuse. Participants were identified as participating in crisis, outpatient, or inpatient leve l s of treatment. Treatment services were identified as: crisis stabilization, detox, therapy/counseling, case management, medication management, residential or psychosocial rehabilitation Social support services include d : housing, employment, vocationa l services, educational services, and benefits coordination. Each was marked with a yes/no as it was possible an individual participated in more than one type of service. Court mandated to the diversion program will be recorded as a yes or no. Length of time in treatment will be recorded as a continuous variable, defined as the

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50 number of days between the date of acceptance and the date of discharge from the program. The data collection form can be found in Appendix A. Null Hypotheses The following hypo theses will be tested in this study: HO1: There will be no differences between the completers, discharged, and non participants in predisposing factors (age, gender, ethnicity, educational level, diagnoses) and criminal history (number of arrests 1 year p re diversion, number of jail days 1 year pre diversion, severity of charges 1 year pre diversion). HO2: There will be no differences between mandated and non mandated participants in post diversion criminal involvement and treatment service utilization usi ng age as a covariate. HO3: There will be no difference s in post diversion number of arrest, number of jail days, and severity of charges when participants (completers and discharged) are compared to non participants. HO4: There are no differences in treat ment service utilization when participants (completers and discharged) are compared to non participants. HO5: Length of time in treatment will not predict post diversion number of arrests number of jail days and severity of charges. HO6 : Housing, emplo yment, vocational services, educational services, and benefits coordination will not predict number of arrests, jail days, or severity of charges. Data Analysis Hypotheses 1 4 will use a parametric or nonparametric chi square, analysis of variance, or lo gistic regression as the analytic procedure. The dependent variables for each of the hypotheses vary in level of measurement and will require different types of analysis. A probability level of p=.05 or less will be used to determine whether to accept or reject the null hypothesis. Gender, educational level, diagnoses, severity of charges, and treatment service utilizat ion are all nominal or ordinal levels of measurement and are not suitable for ANOVA analysis. A Chi Square analysis was used to test for differences between expected and observed frequencies using a probability level of

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51 p=.05 or less to accept or reject t he null hypothesis. Logistic regression allows researchers to identify how well membership can be predicted in a group depending upon a set of predictor variables. Simple and multiple regression analysis were used to test hypotheses 5 and 6. Regression an alysis is not causal in nature and its purpose is the development of an equation for predicting values on a Dependent Variable (DV). The null hypotheses for 5 8 are predictive in nature. Simple linear regression involves a single Independent Variable (IV ) and a single Dependent Variable (DV). The goal of simple regression is to create a linear equation predicting the value of the DV if there is a value for the IV. In multiple regressions, there is a set of predictor or IVs selected to act as of a DV. Mu ltiple regressions are an extension of simple linear regression involving more than one predictor variable and predicts a DV from a weighted linear combination of IVs. One problem with multiple regressions may be the existence of multicollinearity. Multic ollinearity can be problematic if there moderate to high inter correlations between predictor variables. Two or more independent variables may be measuring essentially the same information (Mertler & Vanatta, 2001). Multicollinearity can cause problems wi th the analysis. Stevens (1992) pointed out three reasons why multicollinearity can cause problems. These are: (a) multicollinearity limits the size of the R since the IVs are going after much the same variability in the DV; (b) multicollinearity can caus e difficulty because individual effects are confounded when there is overlapping information; and (c) multi collinearity tends to increase the variance of the regression coefficients resulting in unstable prediction equations. The simplest method of diagn osing multicollinearity is to investigate high inter correlations between the IV predictor

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52 variables. A second method is to inspect the Variance Inflation Factor (VIF), an indicator of the relationship between predictors (Stevens, 1992). Stevens also note d VIF values greater than 10 are generally cause for concern. The data for all regression analyses will be checked to ensure multicollinearity does not present a problem in the analysis. If multicollinearity did exist, a variable will be deleted or varia bles may be combined to create a single construct. The data for the regression analysis will be assessed to make sure the assumptions of regression are met. The assumptions of regression include: (a) the independent variables are fixed (the same values wo uld be found if the study were replicated), (b) the IVs are measured without error, (c) the relationship between the IVs and the DV is co linear, (d) the mean of the residuals for each observation on the DV is zero, (e) errors on the DV are independent, (f ) errors are not correlated with the IV, (g) variance across all values of the IV is constant, and (h) errors are normally distributed (Mertler & Vanatta, 2001). The assumptions will be inspected through examination of residual scatter plots, assessment o f linearity, inspection of normality through skewness, kurtosis, and Kolmogorov for homoscedasticity (Mertler & Vanatta, 2001).

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53 CHAPTER 4 RESULTS The purpose of this study was to examine if participati on in a diversion program reduced recidivism to the criminal justice system as measured by post diversion arrests, jail days, and severity of charges. The study also examined effects of participation on treatment service utilization. Other variables exp lored included the effects of mandated treatment, length of treatment, and the linkage to social support services such as housing, employment, vocational, educational, and benefits coordination as predictor variables for post diversion criminal involvement Each of the research questions was tested for statistical significance using p < 0.05 This chapter present s the demographic information about the sample used in this study the results of data analyses for each research question, and summary of the significan t findings Description of Sample The sample used for this study was selected from the group o f 628 individuals originally accepted for participation in the Forensic Diversion Program between July 1, 2008 and March 27, 2011 Only individuals with complete d intake forms at admission were included this study. Individuals with d uplicate referrals were also removed. If a person was referred multiple times s collected for the time period he/she participated in the program. If the individual was referred multiple times but chose not to participate in the program information from their earliest referral was used. T he final sample included data from 283 individuals. The sample was primarily either African American ( N =157, 55.5% ) or Caucasian ( N =124, 43.8%). The sample also included one Hispanic (0.4%) and one Asian/Pacific Islander (0.4%), with no participants identified as either Native American or Mixed Ethnic. The sample

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54 consisted of more males ( N =209, 73.9%) than females ( N =74, 26.1%). The ages of the sample population ranged from 20 to 7 8 years old, with a mean age of 41.9 years ( SD = 11.658) Approximately half of the sample population had less than a high school education ( N =142, 50.2%), followed by either high school gr aduate/GED ( N =92, 32.5%) and some college ( N =44, 15.5%). Table 4 1 demographic information One of the inclusion criteria for acceptance into the program was a mental health and or substance abuse diagnosis. Of the 283 individuals used in the sample, 153 (54.1%) self reported having a mental health diagnosis and 266 (94.0%) with a substance abuse diagnosis. This compared to 168 (50.4%) individuals diagnosed by staff with a mental health diagnosis and 125 (4 4.2%) with a substance abuse diagnosis. The three mental health diagnoses most reported by the individual included Bipolar Disorder (N=67, 23.7%), Depressive Disorder (N=53, 18.7%) and Schizophrenia (N=33, 11.7%). The three substances used most as report ed by the individual included alcohol (N=164, 58.0%), marijuana (N=87, 30.7%), and cocaine (N=82, 29.0%). Table 4 2 includes the full description of the self reported mental health and substance abuse diagnoses and those diagnosed by treatment staff. Tre atment services and social support services used by the sample population are summarized in Table 4 3. In the sample, 268 (94.7%) of 283 individuals had pre diversion arrests and 281 (99.3%) had served time in jail. On average, participants had 2.98 arr ests ( SD =2.947) with a range of 0 to 24 charges pre diversion and served an average of 57.68 days ( SD =62.975 days) in jail, with a range of 2 to 328 days. Table 4 4 summarizes the criminal involvement variables of pre diversion arrests, pre diversion ja il days, pre

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55 diversion severity of charges, post diversion arrests, post diversion jail days, and post diversion severity of charges. The individuals used in the sample were categorized into one of three status groups: completers discharged or non participants The full description of these three categories is shown in Table 4 5 The discharged group had the highest rate of pre diversion arrests, with a mean of 3.86 ( SD= 3.627) arrests. The complete rs had the highest number of pre diversion jail days, with an average of 68.95 ( SD= 74.118) jail days. No significant analyses were found for pre diversion jail days. The means and standard deviations for the criminal involvement variables are presented i n Table 4 6 separated by the 3 sta tus groups Hypothese s Test and Results This secti on will review the different analyses used for each hypothesis. Hypothesis 1 HO1: There will be no differences between the completers, discharged, and non participants in predisposing factors (age, gender, ethnicity educational level, diagnoses) and crim inal history (number of arrests 1 year pre diversion, number of jail days 1 year pre diversion, severity of charges 1 year pre diversion). To examine hypothesis 1, chi square and logistic multinomial regression w ere used due to the mixture of numerical an d categorical independent variables. A probability level of p =.05 was used for all tests to examine for significant relationships. A logistic multinomial regression was completed using the status group categorization, including completers, discharged, an d non participants. The status group served as the dependent variable or the variable to be predicted. There were three levels of the dependent variable necessitating the use of multinomial regression rather than the dichotomous outcome logistic regressi on. The independent or predictor variables were age, pre diversion arrests, pre diversion jail days, and pre diversion severity of charges.

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56 Model fit indices ( 2 log likelihood = 547.136) and likelihood ratio tests, X 2 (8) = 28.298, p=<.001, found the ful l model predicts significantly better, or more accurately than the null model, indicating a good fit. The pseudo R 2 =.101, the measure of effect size and the metric do not represent the amount of variance accounted for by the predictor variables. Hig her values indicate a better fit but need to be interpreted with caution. The likelihood ratio tests found in Table 4 7 indicate age and severity of charges do not contribute to the model. gro ups. Table 4 8 presents the logistic coefficients ( B ) for each predictor variable of each status group of the outcome variable. The logistic coefficient is the change for each one unit in the predictor variable. The logit is what is being predicted or th e odds of membership in the status group. The closer the coefficient is to zero the less influence it has in predicting the logit. The Wald test is used to evaluate whether or not the coefficient is different from zero. An Exp(B) greater th a n 1.0 will i ncrease the logit and predictors decreasing the logit will have Exp(B) values of less th an 1.0 As seen in T able 4 8 pre diversion arrests affected the discharged group and pre diversion jail days affected non participants. The classification table indi cated 50.6% of the participants were in the correct group (Table 4 9 ). A chi square analysis was used to analyze the nominal variables of gender, ethnicity, educational level, mental health diagnoses, substance abuse disorders and pre diversion severity o f charges Significant chi square differences were found in eth nicity and diagnoses reported by the individual and treatment staff among the status groups. Table 4 10 includes a summary of the test results as differentiated by status

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57 groups. The ethnicit y composition of the completers and discharged groups were fairly evenly split between Caucasian and African Americans, but the group of non participants had nearly twice the amount of African Americans ( N= 80 64% ) as Caucasians ( N= 44 35.2% ). Hypothesis 2 HO2: There will be no differences between mandated and non mandated participants in post diversion criminal involvement and treatment service utilization using age as a covariate An ANOVA was used to examine hypothesis 2. The independent variable of co urt mandated treatment status was determined as yes (1) or no (0). Of the 283 individuals, 49 (17.3%) individuals were court ordered to treatment. No significant differences were revealed betwe en mandated and non mandated participants for post diversion arrests, F (1, 282) = 3.382, p >.05; post diversion jail days, F (1, 279) = 0.418, p > 0.05; or post diversion severity of charges, F (1, 166) = 0.003, p > .05. In looking at treatment service utilization, there was also no differences between mandated and n on mandated participants, F (1, 279 ) = 0.599, p > .05. An univariate analysis of variance was used with age as a covariate, which also resulted in no significant results F (1, 279) = 0.297, p > .05. There were no significant test results, thereby leading t o failure in rejecting the null as related to post diversion criminal involvement and treatment service utilization. Hypothesis 3 HO3: There will be no difference s in post diversion number of arrest, number of jail days, and severity of charges when partic ipants (completers and discharged) are compared to non participants. For hypothesis 3, the status groups of completers and discharged were combined into one, categorized as participants ( N =158, 55.8%). The non participants

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58 ( N =125, 44.2%) remained in a se parate category. An ANOVA compared differences in post diversion arrests for participants and non participants. Significant differences were found between the two groups, F (1, 281) = 5.851, p = 0.016 and the null hypothesis was rejected (Figure 4 1) Program partici pants had a mean of 2.09 ( SD= 3.21) arrests and non participants had a mean of 1.31 ( SD= 1.79). Significant differences between the two groups were also found for post diversion jail days, F (1, 278) = 5.847, p = 0.016 and the null hypothesis was rejected Program participants had a mean of 95.34 jail days ( SD= 88.407) and non participants had a mean of 71.72 ( SD= 70.676) jail days. No significant differences were found between the two groups for post diversion severity of charges, F( 1, 165) = 0.057, p = 0.81 2 therefore the null hypothesis was rejected for this variable Hypothesis 4 HO4: There are no differences in treatment service utilization when participants (completers and discharged) are compared to non participants. For hypothesis 4, the status gro ups of completers and discharged were again combined into one group as participants ( N =158, 55.8%). The non participants ( N =125, 44.2%) stayed in a separate category. ANOVA showed significant differences between the status groups for treatment service ut ilization, F (1, 281) = 150.895, p < 0.001, and the null hypothesis was rejected Participants in the program utilized an average of 2.43 ( SD= 1.32) services, whereas non participants utilized an average of 0.62 ( SD= 1.10). Hypothesis 5 HO5: Length of tim e in treatment will not predict post diversion number of arrests, number of jail days, and severity of charges. Post diversion information was captured from the one year period of time post acceptance to the program. Length of time in treatment was calcul ated by taking the

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59 number of days between the date of acceptance to and date of discharge f rom the program. Time in treat ment ranged from 1 to 766 days, or approximately 1 day to over 2 years. After removing data for individuals with less than 30 days in the program, the average time of participation in the program was 166 days ( SD =140.20 days), or approximately 5.5 months. Analysis of the regression results indicated there was a significant model, R = .124, R 2 = .015, R 2 adj = .012 F (1, 252) = 3.946, p = 0.048, and the null hypothesis was rejected. However, time in treatment only accounted for 1.5% of the variance in post diversion arrests. A significant relationship was found only between length of time in treatment and post diversion arrests, but n ot jail days or severity of charges Table 4 1 1 presents the results of this regression analysis. Hypothesis 6 HO6: Housing, employment, vocational services, educational services, and benefits coordination will not predict number of arrests, jail days, or severity of charges. Social support services were services delivered specifically by the diversion program, including housing, employment, vocational services, educational services, and benefits coordination. Analysis of the regression results show ed a significant model, R = .204, R 2 = .049, R 2 adj = .032 F (1, 277) = 2.840, p = 0.016, and the null hypothesis was rejected. A significant relationship was found between having housing and post diversion arrests, but not jail days or severity of charges Table 4 1 2 includes the results of this regression analysis Change in Severity of Charges Pre diversion and post diversion severity of charges was originally taken as a count of the four different levels of severity: lowest (1), moderate (2), high (3), an d greatest (4). In an attempt to create a more meaningful measure, a new interpretation

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60 of the data was taken by calculating the change in severity between pre diversion and post diversion. No significant differences between the status groups were found for this new variable. A full breakdown of the changes among the different status groups is shown in Figure 4 2. Summary of Findings In this study, the sample used included 158 participants and 125 non participants in the Forensic Diversion Program. Of the 158 participants, 55 (34.8%) completed the program successfully. Prior to participation in the diversion program, the three status groups of completers, discharged and non participants were examined for differences between the groups. Among the pre disposing factors (age, gender, ethnicity, level of education, diagnoses), ethnicity and diagnoses reported by the referred individual and by treatment staff were found to be significantly d ifferent among the three status groups No significant difference s were found among the groups as related to age, gender, or level of education. Significant differences among the groups were also found in pre diversion arrests and pre diversion jail days, but not in pre diversion severity of charges. It is also import ant to note that significant differences among the groups were also found in treatment service utilization, social support services they were linked to, and court mandated status to treatment. T here was a significant difference between groups in court man dated status to treatment, with th e majority of mandated individuals in the completers ( N =24, 43.6 %) and discharged ( N =23, 22.3 %) groups. No significant differences were found when comparing the court mandated participants and the non mandated participan ts on the variable s of post diversion criminal involvement or treatment utilization

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61 When participants in the program were compared to non participants, significant differences were found for post diversion arrests and jail days, as well as treatment ser vice utilization. Figure 4 1 demonstrate s the marked differences between the groups as related to post diversion arrests and jail days When compared on treatment service utilization, the participants had a higher mean of services ( M =2.43, SD = 1. 32) than non participants ( M =0.62, SD = 1.10). Length of time in treatment and social support services were both examined as variables that may predict criminal involvement. It was found that there was a significant relationship between both of these va riables to post diversion arrests only, but not with jail days or severity of charges.

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62 Table 4 1 Characteristics of sample population Frequency (f) Percentage (%) Gender Male 209 73.9 Female 74 26.1 Ethnic group Caucasian 1 24 43.7 African American 157 55.5 Hispanic 1 0.4 Native American 0 0.0 Asian/Pacific Islander 1 0.4 Mixed Ethnic 0 0.0 Educational Level Less than high school 142 50.2 High school graduate/GED 92 32.5 Some college 44 15.5 2 year degree 3 1.1 4 year degree 1 0.4 Graduate degree 1 0.4 Highest grade level if not high school graduate Below 8 th grade 5 3.5 8 th grade 17 6.0 9 th grade 21 7.4 10 th grade 33 11.7 11 th grade 42 14.8 12 th grade 24 8.5 Court mandated to treatment Yes 49 17.3 No 234 82.7

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63 Table 4 2. Self reported and staff diagnosed diagnoses Frequency (f) Percentage (%) Mental health diagnosis Self reported by referred individual 153 54.1 Bipolar Disorder 67 23.7 Depressive Disorder 53 18.7 Schizophrenia 33 11.7 Post Traumatic Stress Disorder 23 8.1 Anxiety Disorder 19 6.7 Attention Deficit Hy peractivity Disorder 10 3.5 Personality Disorder 6 2.1 Obsessive Compulsive Disorder 2 0.7 Diagnosed by treatment staff 168 59.4 Depressive Disorder 70 24.7 Bipolar Disorder 38 13.4 Mood Disorder NOS 32 11.3 Unspecified Mental Disorder 26 9.2 Schizophrenia 24 8.5 Adjustment Disorder 20 7.1 Psychotic Disorder NOS 18 6.4 Anxiety and Panic Disorders 16 5.7 Post Traumatic Stress Disorde r 13 4.6 Substance Induced Mental Disorder 8 2.8 Impulse Control Disorder 1 0.4 Substance abuse diagnosis Self reported by referred individual 266 94.0 Alcohol 164 58.0 Marijuana 87 30.7 Coca ine 82 29.0 Crack cocaine 25 8.8 Opioid 17 6.0 Methadone 1 0.4 Heroin 1 0.4 Diagnosed by treatment staff 125 44.2 Alcohol 60 21.2 Cocaine 5 8 20. 5 Marijuana 14 4.9 Opioid 13 4.6 Polysubstance 11 3.9

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64 Table 4 3 Treatment and social support services Frequency (f) Percentage (%) Treatment Level Outpatient 165 58.3 Crisis 91 32.2 Inpatient 81 28.6 Treatment Services Counse ling 151 53.4 Medication management 97 34.3 Residential treatment 81 28.6 Crisis stabilization 59 20.8 Detoxification 54 19.1 Psychosocial Rehabilitation 11 3.9 Case management 9 3.2 Social Support Services Hou sing 70 24.7 Employment 58 20.5 Vocational services 20 7.1 Benefits coordination 11 3.9 Educational services 10 3.5

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65 Table 4 4. Criminal involvement variables Frequency (f) Percentage (%) Pre Post Pre Post Arrests N one 15 115 5.3 40.6 1 4 218 135 77.0 47.7 5 10 45 27 15.9 9.5 More than 1 0 5 6 1.8 2.2 Jail days None 0 17 0.0 16.1 1 30 (less than 1 month) 138 77 4 9.1 17.5 3 1 90 (1 3 months) 80 80 28. 5 28.5 91 180 (3 6 months) 45 66 1 6.0 23.6 181 360 (6 months 1 year) 18 40 6.4 14.3 Highest severity of charges Lowest (1) 202 107 71.4 37.8 Moderate (2) 33 32 11.7 11.3 High (3) 31 21 11.0 7.4 Greatest (4) 1 7 0.4 2.5 Table 4 5 Participant groups Frequency (f) Percentage (%) Completers 55 19.4 Discharged 103 36.4 Non Participants 125 44.2

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66 Tab le 4 6 Descriptive statistics for age and criminal involvement (CI) N Range Mean SD Status Groups 1 2 3 1 2 3 1 2 3 1 2 3 Age 55 103 125 22 64 21 62 20 78 43.33 42.41 40.79 11.35 11.00 11.66 Pre Diversion CI Arres ts 55 103 125 0 10 0 24 0 23 2.60 3.86 2.41 2.069 3.627 2.453 Jail days 55 102 124 2 327 2 294 4 328 68.95 64.91 46.74 74.118 67.743 51.255 Severity of charges 53 96 118 1 3 1 3 1 4 1.36 1.36 1.37 .710 .682 .714 Post Diversion CI Arrests 55 103 125 0 7 0 19 0 9 0.95 2.70 1.31 1.508 3.688 1.798 Jail Days 55 102 123 0 265 0 357 0 287 75.64 105.96 71.72 71.436 94.964 70.676 Severity of charges 24 75 68 1 3 1 4 1 4 1.38 1.61 1.59 .576 .943 .868

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67 Table 4 7 Likelihood ratio tests 2 Log Likelihood Chi square df p Age 529.680 2.544 2 .280 Pre diversion Arrests 544.986 17.859 2 <.000 Jail days 533.406 6.270 2 .043 Severity of charges 529.299 2.1262 2 .339 Table 4 8 Parameter estimates Status* B Wald df p Exp( B) Confidence Interval 2 Age .018 1.298 1 .255 .982 .953 1.013 Arrest .268 8.762 1 003 1.307 1.095 1.560 Jail Days .005 2.381 1 .123 .995 .989 1.001 Severity of Charges .054 .036 1 .850 .948 .543 1.653 3 Age .024 2.493 1 .114 .977 .948 1.006 Arrest .060 .401 1 .527 1.062 .882 1.278 Jail Days .008 6.118 1 .013 .992 .986 .998 Severity of Charges .262 .943 1 .332 1.299 .766 2.204 *The reference category is 1. Table 4 9 Classification table Predicted 1 2 3 % Correct 1 4 9 40 7.5 2 3 34 58 35.8 3 4 17 96 82.1 Overall % 4.2 22.6 73.2 50.6

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68 Table 4 10 Chi square test results by status group Completers N (%) Discharged N (%) Non Participants N (%) Statistical test and p value Gender 2 (2, N=283)=2.417, p = 0.299 Male 39 ( 70.9 ) 72 ( 69.9 ) 98 ( 78.4 ) Female 16 ( 29.1 ) 31 (30.1 ) 27 (21.6 ) Educational Level 2 (10, N=283)=14.039, p=0.171 Less than high school 28 ( 50.9 ) 48 ( 46.6 ) 66 ( 52.8 ) High school graduate/GED 12 (21.8 ) 42 (40.8 ) 38 ( 30.4 ) Some college 14 (25.5 ) 12 (11.7 ) 18 (14 .4 ) 2 year degree 1 (1.8 ) 0 (0.0) 2 ( 1.6 ) 4 year degree 0 (0.0) 1 (1.0 ) 0 (0. 0 ) Graduate degree 0 (0.0) 0 (0.0) 1 (0.8 ) Ethnic group 2 (6, N=283)=12.825, p=0.046 Caucasian 30 (54.5 ) 50 (48.5 ) 44 (35.2 ) African American 24 (43.6 ) 53 (51.5 ) 80 (64.0 ) Hispan ic 0 (0.0) 0 (0.0) 1 (0.8 ) Native American 0 (0.0) 0 (0.0) 0 (0.0) Asian/Pacific Islander 1 (1.8 ) 0 (0.0) 0 (0.0) Mixed Ethnic 0 (0.0) 0 (0.0) 0 (0.0) Mental health diagnosis Self reported by individual 26 (47.3 ) 66 ( 64.1 ) 61 ( 48.8 ) 2 (2, N=283)=6.575, p=0.037 Diagnosed by treatment staff 45 (81.8 ) 88 (85.4 ) 35 (28.0 ) 2 (2, N=283)=91.494, p=0.000 Substance abuse diagnosis Self reported by individual 52 (94.5 ) 100 (97.1 ) 114 (91.2 ) 2 (2, N=283)=3.503, p=0.173 D iagnosed by treatment staff 33 (60.0 ) 66 (64.1 ) 26 (20.8 ) 2 (2, N=283)=49.827, p=0.000

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69 Table 4 10 Continued Completers N (%) Discharged N (%) Non Participants N (%) Statistical test and p value Treatment Services Counseling 45 (81.8 ) 90 (87.4 ) 16 (12.8 ) 2 (2, N=283)=148.421, p=0.000 Medication management 35 (63.6 ) 46 (44.7 ) 16 (12.8 ) 2 (2, N=283)=51.569, p=0.000 Residential treatment 30 (54.5 ) 43 (41.7 ) 8 (6.4 ) 2 (2, N=283)=56.992, p=0.000 Crisis stabilization 10 (18.2 ) 30 ( 29.1 ) 19 (15.2 ) 2 (2, N=283)=6.931, p=0.031 Detoxification 14 (25.5 ) 29 (28 .2) 11 (8.8 ) 2 (2, N=283)=15.497, p=0.000 Psychosocial Rehabilitation 4 (7.3 ) 3 (2.9 ) 4 (3.2 ) 2 (2, N=283)=2.107, p=0.349 Case management 4 (7.3 ) 1 (1.0 ) 4 (3 .2 ) 2 (2, N=283)=4.625, p=0.099 Social Support Services Housing 30 (54.5 ) 39 (37.9 ) 1 (0.8 ) 2 (2, N=283)=74.256, p=0.000 Employment 27 (49.1 ) 29 (28 .2) 2 (1.6 ) 2 (2, N=283)=58.699, p=0.000 Vocational services 10 (18.2 ) 10 (9.7 ) 0 (0.0) 2 (2, N=283)= 20.945 p=0.0 00 Benefits coordination 7 (12.7 ) 4 (3.9 ) 0 (0.0) 2 (2, N=283)= 13.393 p=0.0 01 Educational services 6 (10.9 ) 4 (3.9 ) 0 (0.0) 2 (2, N=283)= 16.561 p=0.0 00 Court Mandated to Treatment 24 (43.6 ) 23 (22.3 ) 2 (1.6 ) 2 (2, N=283)=49.988, p=0.000 Pre diversion severity of charges 2 (6, N=267)=1.931, p=0.926 Lowest (1) 41 (77.4 ) 72 (75.0 ) 89 (75.4 ) Moderate (2) 5 (9.4 ) 13 ( 13.5 ) 15 (12.7 ) High (3) 7 (13.2 ) 11 (11.5 ) 13 (11.0 ) Greatest (4) 0 (0.0) 0 (0.0 ) 1 (0.8 )

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70 Table 4 1 1 Regression table for length in treatment and post diversion arrests SE p Length in Treatment 0.002 0.001 0.124 .048 Notes: R 2 = 0.015 (p = 0.048) Table 4 1 2 Regression table for socia l support services and post diversion arrests Social support services SE p Housing 1.275 0.409 0.204 .002 Employment .056 .433 .008 .897 Vocational services .952 .706 .090 .178 Educational services 1.003 .885 .069 .258 Benefits coordination .958 .932 .069 .305 Notes: R 2 = 0.049 (p = 0.016)

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71 Figure 4 1. Comparison of pre diversion and post diversion rates between participants and non participants for A) arrests and B) jail days.

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72 Figure 4 2. Change i n severity of charges, differentiated by status groups.

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73 CHAPTER 5 DISCUSSION With the growing jail and prison populations and limited space in these facilities, diversion programs have been explored as a possible option for those needing mental health and/or substance abuse treatment. Factors relating to the participant and treatment were examined in this study, and their effects on reducing post diversion criminal justice involvement and treatment service utilization. The purpose of this study was to exam ine if participation in a diversion program reduced recidivism to the criminal justice system as measured by post diversion arrests, jail days, and severity of charges. The study also examined effects of participation on treatment service utilization Disc ussion of Findings The sample used in this study was gathered from reviewing secondary information collected by a local jail diversion program. It did not include a completely randomized sampling, as only the non participants were randomly selected, and a ll of the participants in the program were included in the sample. The demographics of the sample population were not representative of the resid ents in this area. The sample comprised of 55.5% African Americans, and 43.74% Caucasians with an under representation by all the other ethnic groups. The sample also was comprised of mostly males (73.9%). Approximately 50% of the sample had less than a high school population, with 9.5% having less than an 8 th grade education. Of the individuals who did not complete high school or obtain their GED, most individual s dropped out in the 10 th ( N= 33, 11.7%) and 11 th ( N=42 14.8%) grades. This information may be useful for prevention related programs if looking to start interventions early during the ind years in school.

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74 A number of the individuals referred to the program reported having a mental health ( N= 153, 54.1%) or substance abuse ( N= 266, 94.0%) diagnosis. The top three diagnoses self reported by the referred individuals were simila r to those found by Broner, Mayrl, and Landsberg (2005) in a New York City jail. For this sample, the top three reported mental illnesses were Bipolar Disorder, Depressive Disorder, and Schizophrenia. The top three diagnosed by staff were Depressive Diso rder, Bipolar Disorder, and Mood Disorder. Something interesting for this particular sample population was that 94% reported having a substance abuse issue. While this contrasted with the 44.2% substance abuse diagnoses reported by staff, this may be due to the recording procedure by the treatment agency. Clinically, a substance abuse issue is addressed prior to the treatment of a mental health issue but documentation at th is r treatment. One possible reason for the difference between self reported mental health and substance abuse diagnoses by the individual is the increased awareness a person has of their substance abuse issue s versus underlying mental health issue s especia lly if the person has never been in professional treatment. The high number of self reported diagnoses may also be due to the individuals knowing that participation in the program would divert them from further incarceration. In regards to treatme nt services utilized, the services individuals engaged in at the agency since 2007 and used to capture treatment information for this study. The treatment services were recorded and divided into t hree treatment levels, including outpatient, crisis, and inpatient. Over half ( N= 165, 58.3%) of the sample population utilized outpatient

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75 services at some point in time during this time period, with about 32% having used crisis level services, and approxi mately 29% utilized inpatient services. Outpatient services, most specifically counseling services, may have been the most utilized treatment service due to it being the least invasive level of treatment when compared to residential or crisis level se rvices. In addition, case management and psychosocial rehabilitation services typically required the individual to have a private funding source. Included in the services offered by the Forensic Diversion Program, participants were referred to social support services such as linkages to housing, employment, vocational services, educational services, and benefits coordination. These services have been found in previous studies (Broner et al., 2003) as services needed by individuals coping with co occur ring mental health and substance abuse disorders. Of the social support services, housing was the most utilized service ( N= 70, 24.7%), followed by active employment ( N= 58, 20.5%). Housing was an important component t as this often offered a change in their previous environment which fostered continued substance use or criminal behavior, making them a high risk for recidivism in the criminal justice system and their recovery Hypothesis 1 Research results indicated there were some characteristics identified common to participants of diversion programs. The first research question addressed differences among the three status groups used in this study, including participants completing treatment (completers), participants engaged in treatment but were discharg ed prior to the study with successful completion (discharged), and individuals accepted to the diversion program but not participating in treatment (non participants). Significant differences were found among the groups in ethnic composition, with the non

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76 participants having nearly double ( N= 80, 64.0%) the number of African Americans as Caucasians ( N= 44, 35.2%). The other two status groups were fairly evenly split between the 2 ethnicities. S ignificant differences between the groups were found for self reported mental health diagno ses, and in both mental health and substance abuse diagnoses reported by staff. Participants who were discharged from treatment prior to completion had the highest rate of self reported mental health diagnose s. Almost 65% of individuals in the discharged group self reported a mental illness compared to 47% of completers and 49% of non participants. Significant differences between the groups were found with both mental health and substance abuse diagnoses by staff, in which there were higher rates for both the completers and discharged groups, c ompared to the non participants. This could be due to staff having just one opportunity to assess the individual, as ongoing assessment was not possible due to the individual not participating in further treatment. Although there were no significant differences found with self reported substance abuse issues, i t was also noted all three status groups had over a 90% self reporting rate In regards to the criminal history, signifi cant differences were found between the three g roups. Strictly looking at the averages for pre dive rsion arrests among the three groups ( Table 4 6) the completers ( M= 2.60, SD= 2.069) and the discharged ( M= 3.86, SD= 3.627) group had higher mea ns when compar ed to non participants ( M= 2.41, SD= 2.453) The findings of this study were different from previous studies (Broner et al., 2004 and Steadman & Naples, 2005). Previous studies found diversion program individuals had fewer prior arrests and spent more days in jail prior to treatment engagement than non diverted individuals. With this study sam ple, the discharged

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77 group had the highest number of pre diversion arrests and the completers had the highest number of pre diversion jail days. Both of these groups consisted of diversion program individuals. Lattimore et al. (2003) noted the importance of examining the target groups for diversion programs. T he Forensic Diversion Program was targeted to serve the individuals identified as chronic recidivists in the criminal justice system. The majority of the individuals accepted for the program were th ose with multiple misdemeanor charges. It would be important in future research to assess what other variables affect the mo tivat ion of an individual to participate in treatment Hypothesis 2 There were no significant findings for this study when investigating court mandated treatment in relation to post diversion criminal involvement and treatment service utilization. This finding was surprising, as previous studies (Broner et al., 2005; Lamb et al., 1996) have found better outcomes and longer durations in treatment among court mandated participants. The lack of a significant finding could have been attributed to the low number of court mandated individuals ( N= 49, 17.3%) to treatment in this sample Working with indivi duals in the program, participants often requested not to be court mandated to treatment if given a choice. An example of this would be if termination of probation was contingent on successful completion of treatment. In many cases, participants chose to complete a jail sentence in order to be free of all probation and treatment requirements. Draine and Solomon (2005) acknowledged higher re arrest rates have been found for individuals under probation supervision or monitoring. Variables for future resear including violations and completions, and a lso assess a n individual s level of treatment readiness

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78 when mandated to treatment. Another variable that will be important to assess in future research is the notion of perceived coercion by the individual. Cus ack, Stea dman, and Herring (2010) assert an understanding of this factor which is defined as an individua l s perceived lack of choice or control, lends a perspective on how diversion programs can engage and maintain participants. This factor is also important as court mandat ed treatment is essentially still voluntary for the ind ividual. The individual ultimately has the option of accepting or denying this order, as declining the offer would return the person to the regular proceedings of sentencing and completion of a jail or prison term Hypothesis 3 This hypothesis sought to assess more directly the impact of participation in a diversion program. The completers and discharged groups were combined into one participant group. Significant differences were found for both post diversion arrests and jail days. The participants had a higher mean score for both of these measurements However, it was noted there was a significant decrease between pre diversion ( M= 3.42) and post diversion ( M= 2.09) arrests. Jail days increased from an average of 66 days pre diversion to 95 days, but there were several scenarios to explain this phenome non. First, participants in treatment had higher means of both pre diversion arrests and jail days when compared to non participants. In addition, i ndividuals may have needed to remain in jail for longer periods of time while the diversion program worked out an agreeable plan with the court for release. Individuals also might be re incarcerated for longer periods of time if they reoffended whi le in treatment, or decided later to no longer engage in treatment. It is important to note here that 40.6% ( N =115) of th e sample did not re offend post diversion thereby reducing these individua ls recidivism rates within 12 months post diversion It should also be noted tha t f or arrests rates there was a

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79 63.5% reduction for completers and 30.1% reduction for discharged participants This finding is congruent with those of Case et al. (2009) in their finding of 47.3% of diversion participants in their study had no further re arrests These findings are comparable to previous research. In a sample of incarcerated inma tes in 2005 Torrey et al. (2010) found offenders with mental illness to have higher recidivism rates when compared to other released individuals. Individuals who participated in diversion were also more likely to report participation in treatment service s (Steadman and Naples, 2005). Taking into account that the target groups of diversion programs need to be considered when making comparisons (Lattimore et al., 2003), the target population for this program included chronic recidivists in the system, who have established patterns of behavior in addition to more complex needs as related to mental health and/or substance use is sues, treatment, and basic needs It may take a few episodes of treatment before new behavior patterns emerge. For this reason, it may be helpful in future research to look at rediversion, which has been defined as a former or current diversion program participant being bo oked into jail on a new charge and being diverted once again through t he same diversion program ( Boccaccini, Christy, Poythress & Kersha 200 5 p. 836). In their study looking at two diversion programs in F l orida, Boccaccini et al. (2005), 21 2 2% of participants had more than one instance of re diversion from the criminal justice system Of those who were re diverted, one fifth of the individuals were rediverted within 30 days of their initial one, with half by 90 days, and three quarters by 180 days. They suggest further examination of this factor could help fu ture diversion programs i dentify those individuals who would be at high risk for rediversions, and make necessary treatment accommodations

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80 Hypothesis 4 This question explored the variable of treatment service utilization. While treatment use could not be attributed directly to partici pation in the diversion program there was a higher treatment utilization rate among participants ( M= 2.43, SD= 1.32) versus non participants ( M= 0.62, SD= 1.10). This factor alone may explain why participants engaged in the diversion program as compare d to the non participants having been previously engaged in services. As previously mentioned, outpatient services were the most utilized treatment service, specifically counseling services ( Table 4 10). This finding parallels those of Jaffe, Du, Huang, and Hser (2012), where an analysis of over 1000 treatment participants showed that 77.6% attended outpatient treatment. Though residential treatment was not as wi dely used (17.5%), their review of research has shown residential treatment producing more successful treatment outcomes than outpatient treatment ( Broner et al., 2005; Jaffe et al., 2012). Hypothesis 5 Previous research has demonstrated success of diversion treatment programs in reducing recidivism, but one problem has been found to be with treatment attrition (Olver e t al., 2011). Olver et al. (2011) noted in their review of 114 offender treatment studies with over 41,000 offenders, the treatment attrition rate was approximately 27.1%. The attrition rate for this sample was high with 103 of 158 (65%) participants dr opping out of treatment either by their own choice or for treatment non compliance. Of the participants, 35% ( N= 55) were successful completers of the program. The average length of time in treatment between the completers and discharged group found comp leters remained in the program approximately 75 days longer or 2.5 more

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81 months. The average length of stay for the completers was 274 days ( SD= 146.60) or approximately 9 months. This was longer than the original program expectation of 120 180 days (4 to 6 months). Hypothesis 6 A significant relationship was found between linkage to social support services and post diversion criminal involvement, specifically between the linkage to housing and post diversion arrests. Housing in this diversion program w as provided as a resource for participants who were able to demonstrate consistent engagement with treatment after their release from jail. Many of the participants were initially either homeless or had temporary housing options with friends or family u pon jail release. Continued homelessness often led to disengagement with treatment because the individual returned to a previous lifestyle increasing the vulnerab ility to r e lapse, r e arrest or re incarceration. Though resources for housing were limited, this is a resource t hat sho uld be emphasized for future diversion programs. As mentioned by Case et al. (2009), an individua l s housing status was a significant predictor for post diversion arrests. Conclusions This study certainly adds to the existing research on diversion programs. Significant predictors of participation were found in this program including ethnicity, diagnoses, pre diversion arrests and pre diversion jail days. Participation in the diversion program had significant impacts on post diversion arrests, post diversion jail days, and treatment service utilization. Length of time in treatment and linkages to social support services, par ticularly housing, were significant predictors of post diversion arrests.

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82 Limitations of the Study The overall results of this study may be generalizable to jail diversion programs however certain limitations need to be discussed t o understand the results within the context of the study. This section will review limitations of this study possibly compromising the validity and generalizability of the findings. Due to utilizing secondary data, m easurements for the variables were lim ited to the existing data and outcomes already collected by the diversion program studied The variables for the study were defined and collected by the primary investigator. Although there was only one primary investigator for this study, limiting any i nter rater reliability issues, the clinical interpretation of the variables during the data collection process was attributed to this one investigator. These issues could affect the face validity of the study, in whether appropriate variables were selecte d to measure outcomes of the diversion program, and if the variables were accurately measuring what were identified for the study. In addition, t he outcome data collected by the diversion program w as based upon the clinical judgments of different clinici ans with varying levels of experience in the field. The diagnostic impression, treatment services, and socia l support services needed by the participants could have varied depending on the individual treatment staff. The diversion program did implement r outine meetings, not just among treatment staff, but also with representatives from the court, the jail, and the county to review cases and This study was also limited in the structure and design of the diversion program. The Forensic Diversion Program was newly implemented in July 2008 and was required to undergo numerous programmatic and clinical changes throughout the three year course of the grant. The program included the key activities of a diversion progra m,

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83 including access to treatment, decreasing the length of incarceration, (Broner et al., 2004; Draine & Soloman, 2005; Steadman et al., 2005). However, n o two diversion programs are exactly the same As cautioned by Lattimore, Broner, Sherman, Frisman, and Shafer (2003) in comparing programs with each other, diversion encompasses a range of program types that vary: on the stage at which and location from which the diversion occurs; on whether, and the extent to which, criminal justice and services monit oring occurs postdiversion; in terms of the degree of involvement in the community service delivery system; and on the target population (p. 58) In 2009, Parker, Foley, Moore, and Broner examined four diversion programs in Florida, Nebraska and New York, outlining challenges in implementing these programs and possible solutions to promote stakeholder buy in. Each of these programs experienced challenges ranging from establishing credibility, easing concerns of public safety, to staff turnover. It would appear that each diversion program, no matter how well planned, will need a period of working through issues during the implementation process. Another design related limitation was the cross sectional design of the study, where many of the variables were report at the time of their intake evaluation. This was a direct effect of utilizing secondary data, as the variables defined for the study were largely collected only at the intake phase. Recidivism data used in this study was limited to 12 months post diversion. Outcome data related to treatment was collected for program participants, but not systematically collected at certain points of time This data was also not available if an individual was discharged prior to program completion o r if the person stopped engaging in treatment I f a longitudinal design was used for future studies it is recommended that the to be collected at other points in the treatment process, including

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84 engagement, 3 and 6 months into treatment, and at discharge from the program. Post diversion outcome s should also be tracked over longer periods of time, as previous research show that diversion related out comes do not always sustain over time. The of motivation for treatment and the perception of the benefits of treatment, treatment needs, and important aspects of the diversion program that help reduce recidivism to the jail system would be data recommended for future studies. These are all dynamic variab les that could potentially change as the individual progresses through treatment In addition, collecting both self report ed and staff perceptions could be beneficia l for comparison. reported responses through a structured in terview, as is common with initial evaluations, can create another potential bias of the individuals giving socially desirable responses. Social desirability becomes a factor due to the interaction with the evaluator ( Dillman, 2007 ). This could impact re sponses to include exaggerated information to present a worse situation, or under reported information to minimize the situation. Future research may consider using diagnostic tests or standardized screening tools or implementing an evaluation process where the individual would not feel inclined to give socially desirable responses Selection bias m ay have also affected the results The majority of the intakes were completed while the individual was still incarcerated, lending some incentive for the individual to be screened for a diversion program. Most of the individuals would have the knowledge the diversion program could help them get released from the jail or at least reduce the number of days they were incarcerated. This may have influenced individuals to report the presence of certain variables, such as a mental health or

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85 substance abuse diagnosis since this was part of the criteria for acceptance in the diversion program. Other well known benefits of being in the program included the possibility of obtaining resources, of which housing was the social support service most individuals requested. The diversion pr ogram may disproportionately attract these individuals, making it difficult to determine if the study results was skewed in a positive or negative direction Though the study examined correlations between variables, a significant relationship does not imp ly causation. Both of the correlations ( R 2 ) examined in this study were not particularly strong, therefore other variables may have influenced these relationships. Implications There are a few considerations for future research to be discussed here This study incorporated information only from individuals referred and accepted to participate in the Forensic Diversion Program. The intake evaluation was completed only when the individual was thought to have a mental health and/or substance abuse is sue that could benefit from treatment. Future research could also include information from a control group more representative of the general jail or prison population. This might be another way to determine if the reduction in r ecidivism rates were dire ctly impacted by participation in a diversion program. This study was also limited to secondary data Should this same study be replicated in the future, a mixed method design is recommended to allow participants to be interviewed to gain a bett er understanding of the benefits of treatment and the effects of incarceration and diversion. Again, collecting the data at multiple points in the depth understanding

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86 of what constitutes a s uccessful diversion program, what would promote successful completion of treatment and what factors are vital for continued success Future research may also need to consider the implications of having multiple stakeholders involved with a diversion project Each entity will have its own desired ou tcomes, and there may be differences in how certain things are defined (CMHS National GAINS Center, 2007). This includes the difference in how d iversion is define d by t he criminal justice system and treatment providers. GAINS Center for Behavioral Health and Justice Transformation note that criminal justice professional may drop or not pursue charges in the individual agrees to treatment, therefore relieving the court of any continued court oversight. For mental health professionals the goal for diversion is to create an alternative to incarceration, and could potentially include continued court oversight. This may propose an issue for some buy in into a diversion program, as some individuals with minor charges may have shorter per iods of involvement with the court and the jail systems by simply having their charges resolved without any diversion consideration (Roesch, 1995). This study highlights the differences between an empirical study and program evaluation but also that the y are often not mutually exclusive Coffman (2003) that is, observed, measured, or calcula Small (2012) pointed out four major ways program evaluation differs from research: 1) its focus on a program versus a population, 2) it improves versus proves, 3) it determines value versus being value free, During the three year course of the

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87 diversion program used in this study, t presented to a large stakeholder committee to ensure that the program was staying true to the program objectives o f reducing recidivism and diverting individuals to appropriate treatment services. Based on these ongoing evaluations, the program was deemed as maki ng a difference and was meeting established objectives. Using the empirical design of this study, the difference was not significant based on the statistical analyses of the collected data. This may imply researchers, program developers, and clinicians all need to communicate and work together in defining variables and outcome measures, setting up the program design, and determining the data collection process Implications for Theory The results of this study are helpful for clinicians working with this population. kes it necessary to consider the complexities treatment programs and interventions have to address. Not only are treatment staff members working with the individual but also the barriers and limitations existing in the different systems of the jails, cour ts, and community. F or clinicians working with this population their training and basic knowledge base needs to incorporate more than just treatment modalities and interventions. The theoretical frameworks used for this study included Self Control Th eory (Gottfredson & Hirschi, 1990) and the Transtheoretical Model (Prochaska & Declemente, 1982) Both of these models provide d additional considerations when looking at client factors such as treatment resistance or attrition as applied to this populat ion Rotter et al (2005) proposed that treatment providers are responsible for developing cultural competence in working with this population, increasing awareness and broadening perspectives of an

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88 The theories a lso encourage clinicians to examine how this pattern of chronic incarcerations developed, and what needs the program must fulfill. This offers new meaning to the concept of counseling is like peeling an onion, with the need to remov e multiple layers befor e the underlying issues can be discovered. Another implication is the fluidity of working with this population. The program is based on the SIM, which emphasizes the multiple entry points an individual could enter the criminal justice system, and hope fully enter into treatment. This model highlights the importance of working with the entire system, starting within the treatment agency, The fluidity also applies to the in dividual, who may cycle through the various stages of change. The individual may also transition quickly through different states of being unstable, homeless, or substance dependent. The clinician must encompass the ability to quickly assess and determin immediate risk factors and treatment needs. s or resistance s to treatment. Implications for Practice The linkage of individuals to appropriate mental hea lth and substance abuse treatment services has been increasingly discussed in the media as well as in policy reform. Considerations for programs have to focus on those producing the best outcomes as resources are limited. The population currently studied includes individuals at the highest risk, coupled often with having the least resources. Having studies such as the current study can help identify the important variables to focus on in treatment as well as predictors for success.

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89 The complexities of working with this population magnify the issue of program sustainability, and how staff turnover can be a significant challenge Research has shown a turnover rate of 25% for direct care staff and 54% for program directors of substance abuse programs (Kni ght, Becan, & Flynn, 2012). The coming and going of staff in a program impacts not only the organization but could have an impact on program implementation, operations, and outcomes. Barner, Hunter, Modisette, Ihnes, and Godley (2012) lists the two most common consequences of staff turnover involves the financial cost of replacing staff, and also a reduction in the quality of services delivered to clients. In the three years examined in this study, the Forensic Diversion Program experienced three major c hanges in clinical staff. Supervision and training of staff working with this particular population need to maximize efforts in helping the clinician process about burnout and job satisfaction to reduce staff turnover. Summary This chapter provided a dis cussion of the results of this study examining the effects of participation in a diversion program on recidivism to the criminal justice system and treatment service utilization, which are both indicators for successful community integration. Limitations of the present study were reviewed, concluding with a discussion of the implications for future research, theory, and practice.

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90 APPENDIX A DATA COLLECTION FORM ID Number ___________________________ Diversion Program Data Collection Participant Stat us Participant Gender Age _____________________ Record year of birth __________________________ L evel of education ar degree Ethnic group Diagnosis e Abuse ______________________________ Treatment Level Treatment Utilization Residential Psychosocial Rehabilitatio n Social Supports

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91 Criminal Involvement Number of arrests in year prior to admission to program ___________ Number of days in jail in year prior to admiss ion to program _____________ Number of arrests in year post admission to program ___________ Number of days in jail in year post admission to program _____________ Severity of charges 1 year post admission: Court Mandated Duration in Treatment Date of Acceptance ________________ Date of Discharge ________________ Extras Physical Health Major medical problems/ chronic illness _______________________________ Head injury _____________________________________________________

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92 APPENDIX B FORENSIC DIVERSION TEAM INTAKE FORM

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96 APPENDIX C 1 year prior to decision date Decision Date # of charges (1 year prior) Dates charges # of NEW incarcerations (1 year prior) Dates of incarcerations # of incarceration days (1 year prior) Highest severity of charge 1 year pre acceptance 6/26/2009 1 06/04/09 Disturbing Peace: Br each Peace (2M) 2 08/05/2008 08/20/2008 06/04/2009 07/11/2009 39 Lowest 6/26/2009 1 04/08/09 Marijuana possess: Not More Than 20 Grams (1M) 3 11/21/2008 12/02/2008 04/20/2009 05/07/2009 06/10/2009 08/16/2009 47 Lowest 11/5/2010 1 10/17/10 D rive While Lic Susp 2nd Off (1M) 1 10/17/2010 11/09/2010 20 Lowest 3/27/2009 4 06/30/08 Nonmoving Traffic Viol: Drive While Lic Susp 1st O (2M) 01/25/09 Vehicle Theft: Grand Third Degree (3F) 01/25/09 Obstructing Justice harass Witness Victim Or I nfor (1M) 03/12/09 Condit Release Violation: Pre Trial Release Cond V (1M) 3 08/07/2008 08/08/2008 01/25/2009 01/25/2009 03/12/2009 03/20/2009 12 High 11/15/2010 1 06/12/10 Disorderly Conduct (2M) 2 06/12/2010 06/13/2010 11/03/2010 02/25/201 1 15 Lowest Meridian Behavioral Healthcare, Inc. (2011). Criminal history database [Data file].

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97 APPENDIX D SEVERITY OF CHARGES CHART Greatest Severity High Severity Moderate Severity Lowest Severity Attempted Murder Agg. Assault w/deadly weapon Access ory (Felony) All other misdmeanors Attempted Sexual Battery Agg. Battery Agg. Assault Bigamy & polygamy Carjacking Agg. Child Abuse Agg. Fleeing Child abuse (misdemeanor) Escape from Secure Facility Agg. Stalking Burglary of unoccupied structure, dwelli ng or conveyance Child neglect Home invasion (armed) Armed burglary Child abuse (Felony) Contributing to delinquency of minor Homicide Arson Counterfeiting Criminal mischief (Felony or Misdemeanor) Kidnapping Assault or battery on L.E. or EMS Personell Dealing in stolen property (and Organized) Defrauding an Innkeeper L & L Child under 12 Assault with Intent to Kill Driving under the influence (DUI Felony) Disorderly conduct Murder 1st Degree Attempted Arson DUI (Felony) Driving under the influence (D UI misdemeanor) Murder 2nd Degree Attempted Kidnapping DWLSR (Habitual) Dumping industrial substance for commercial purposes Murder 3rd Degree Battery on a detainee by detainee Embezzlement Exposure of sexual organs Robbery with Firearm Battery on a p regnant victim Escape from police or non secure facility Felony petit theft Sexual Battery Battery on person 65 years or older Exploiting the elderly Gambling laws violation Sexual Predator Burglary of occupied dwelling Fleeing & attempt to elude (Felony ) Indecent exposure Sodomy Child fondling Forgery INS/Immigration Hold (Unless additional charges) Drug Trafficking Fraudulent us of a credit card Interferring with child custody Explosives (sale, Poss/Use) Grand Larceny Larceny Failure to report change of address w/in 48 hrs Grand Theft Narcotics Violations (Misdemeanor) False Imprisonment Grand theft auto open container Felony Battery Hit & Run Operating a house of Ill Repute Felony Murder Impersonating a law enforcement officer Panhand ling Firearms Trafficking Introduction of contraband into Jail Poss of a cont. subst. (or substance in lieu of) Terrorist Act(s) L&L charges with misdemeanor case number Trespassing Harboring a fugative Leaving scene of accident with with injury P oss of drug paraphernalia Harrass victim,witness or informant Maintaining a residence where drugs are sold Poss of stolen property Home Invasion Manslaughter Involuntary (including DUI Manslaughter) Probation violation technical L & L with a Minor (All) Negligent and Involuntary Homicide Prostitution (Felony or Misdemeanor) Manslaughter, Voluntary Obstruction of justice Public intoxication Military Desertion Poss of prohibited weapon (Felony) Resisting without violence Poss of a concealed FA Resisting with violence Selling liquor without a license Poss of a concealed FA by a convicted felon Sale or poss with intent to sale or distribute Shoplifting Poss of FA during commission of a felony Tampering with vic/witness (Felony) Soliciting prostitution Racketeering & Extorion Tax law violation Stalking Repeat offender Threatening a public servant Tamper with or fabricate physical evidence Robbery (armed) Uttering a forged instrument Tampering with vic/witness (Misd) Robbery by sudden snatching VOP Non Technical Theft (all misdemeanor thefts) Shooting into an occupied dwelling Welfare fraud Traffic laws Throwing a deadly missile Crimes Against Person Public Servant / Family Urinating in public Trafficking Violation o f pre trial release Robbery (unarmed) Alachua County Jail (2008). Severity of Charges matrix [ Data file ]

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98 LIST OF REFERENCES Andrews, D., & Bonta, J. (2006). The psychology of criminal conduct, 4th Ed. Newark, NJ: Anderson Publishing. Andrews, D. A., Bonta, J., and Hoge, R. D. (1990). Classification for effective rehabilitation. Criminal Justice and Behavior 17 (1), 19 52. Ary, D., Jacobs, L., Sorenson, C., & Razavieh, A.,.(2009). Introduction to research in education (8 th ed). Belmont, CA: Wadswor th. Benda, B. B. (2003). Survival analysis of criminal recidivism of boot camp graduates using elements from general and developmental explanatory models. International Journal of Offender Therapy and Comparative Criminology, 47 (1), 89 110. Bittner, E. (1 967). Police discretion in emergency apprehension of mentally ill persons. Social Problems, 14 (3), 278 292. Bloom, J. D. (2010). "The incarceration revolution": The abandonment of the seriously mentally ill to our jails and prisons. Journal of Law, Medicin e, & Ethics 38 (4), 727 734. Boccaccini, M. T., Christy, A., Poythress, N., & Kershaw, D. (2005). Rediversion in two postbooking jail diversion programs in F lorida. Psychiatric Services, 56( 7), 835 839. Bordin, E. S. (1979). The generalizability of the psychoanalytic concept of the working alliance. Psychotherapy: Theory, Research, and Practice 16 (3) 252 260. Broner, N., Lamon, S. S., Mayrl, D. W., & Karopkin, M. G. (2003). Arrested adults awaiting arraignment: Mental health, substance abuse, and criminal justice characteristics and needs. Fordham Urban Law Jour n al, 30 (2), 663 721. Broner, N., Lattimore, P. K., Cowell, A. J., & Schlenger, W. E. (2004). Effects of diversion on adults with co occurring mental illness and substance use: Outcomes from a national multi site study. Behavioral Sciences & the Law, 22 (4), 519 541. Broner, N., Mayrl, D. W., & Landsberg, G. (2005). Outcomes of mandated and nonmandated New York City jail diversion for offenders with alcohol, drug, and mental disorders. The Prison Journal 85 (1), 18 49. Butzin, C. A., Saum, C. A., & Scarpitti, F. R. (2002). Factors associated with completion of a drug treatment court diversion program. Substance Use & Misus e, 37 (12 13), 1615 1633.

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102 Rotter, M., McQuistion, H. L., Broner, N., & Steinbacher, M. (2005). The impact of the group treatment model. Psychiatric Services, 56 (3), 265 267. Slate, R. N.(2009). Seeking alternatives to the criminalization of mental illness. American Jails 23 (1), 20 28. Small, P. (2012, January 8). Four differences between research and program evaluation [Web blog posting]. Retrieved from http://managementhelp.org/blogs/nonprofit capacity building/2012/01/08/four differences between research and program evaluation/ Steadman, H. J., Deane, M. W., Morrissey, J. P., Westcott, M. L., Salasin, S., & Shapiro, S. (1999). A SAMHSA research initiative assessing the effectiveness of jail diversion programs for mentally ill persons. Psychiatric Services 50 (12), 1620 1623. Steadman, H. J., Morris, S. M., & Dennis, D. L. (1995). The diversion of mentally il l persons from jails to community based services: A profile to programs. American Journal of Public Health, 85 (12) 1630 1635. Steadman, H. J., & Naples, M. (2005). Assessing the effectiveness of jail diversion programs for persons with serious mental illn ess and co occurring substance use disorders. Behavioral Sciences & the Law, 23 (2), 163 170. Steadman, H. J., Osher, F. C., Robbins, P. C., Case, B., & Samuels, S. (2009). Prevalence of serious mental illness among jail inmates. Psychiatric Services, 60 (6) 761 765. Stevens, J. (1992 ) Applied multivariate statistics for the social sciences. Hillsdale, NJ: Lawrence Erlbaum Associates. Substance Abuse and Mental Health Services Administration. (2010). Mental Health, United States, 2008. Washington, DC: Susta nce Abuse and Mental Health Services Administration, U.S. Department of Health and Human Services. Sung, H., Mellow, J., & Mahoney, A. M. (2010). Jail inmates with co occurring mental health and substance use problems: Correlates and service needs. Journal of Offender Rehabilitation 49 (2) 126 145. Talley, B. S., & Coleman, M. A. (1992). The chronically mentally ill: Issues of individual freedom versus societal neglect. Journal of Community Health Nursing 9 (1), 33 41. The Criminal Justice, Mental Health, and Substance Abuse Technical Assistance Center. (2012). Annual report on the criminal justice, mental health, and substance abuse reinvestment grant program act. Retrieved October 16, 2012,

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104 BIOGRAPHICAL SKETCH Shinlay Chu Rivera was born in Taiwan and came to the United States a t the age of three After graduating from Northeast High School in St. Petersburg, Florida, she starte d her undergraduate program in psychology and s ociology at the University of Florida. She continued her graduate studies in the Departme nt of Counselor Education and obtained her Master of Education and Specialist in Education degrees in Marriage and Family Therapy in 2004 She was employed with Meridian Behavior Healthcare, a local community mental health agency, when she returned to the University of Florida for the doctoral program. Her experience in agency work included crisis intervention and stabilization, treatment of co occurring disorders, forensics, primary behavioral healthcare, and family services from both a practitioner and managerial role She completed her doctora te program in Marriage and Family Therapy in 2013 at the University of Florida.