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Recidivism of Offenders with Mental Illness Released From Prison to an Intensive Community Treatment Program

NCJ Number
224475
Journal
Journal of Offender Rehabilitation Volume: 47 Issue: 4 Dated: 2008 Pages: 385-406
Author(s)
Gregory Theurer; David Lovell
Date Published
2008
Length
22 pages
Annotation

After describing the features of an intensive case-management treatment program for mentally ill offenders (MIOs) in Washington State, findings are presented on the recidivism of participants.

Abstract

The evaluation findings show that significant reductions in expected rates of recidivism can be achieved among MIOs through a program that combines interagency collaboration; housing support; and intensive, integrated clinical attention to mental health and substance abuse problems. Participants in Washington State’s Mentally Ill Offender Community Transition Program (MIOCTP) were less likely to commit new felonies or any new crime than a comparison group of offenders matched on a set of variables that predicted recidivism. The program was designed to address three gaps in provisions for MIOs released from prison: housing, prompt access to clinical services tailored to their needs, and cooperation between the public agencies charged with serving or managing them. Whether this pattern of success can be maintained over time and whether similar programs in other locations can achieve similar benefits must be determined in future research. Several recent recidivism studies in Washington State (Lovell et al., 2002; Lovell, Johnson, and Cain, 2007) provided a dataset of 1,362 offenders with mental illness released from prison during 1996-98. Using this group of offenders and following techniques applied in Lovell et al. (2007), this study used a retrospective matched control design in which the influence of confounding variables was contained by matching MIOCTP subjects and controls on eight predictors of recidivism. Selection of predictors was informed by the results of other studies and developed and tested by logistic regression techniques. 4 tables and 23 references