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Relationship between network clustering in a therapeutic community and reincarceration following discharge

Published on Feb 1, 2019in Journal of Substance Abuse Treatment2.542
路 DOI :10.1016/j.jsat.2018.10.008
Benjamin W. Campbell2
Estimated H-index: 2
(OSU: Ohio State University),
Skyler J. Cranmer16
Estimated H-index: 16
(OSU: Ohio State University)
+ 1 AuthorsKeith Warren10
Estimated H-index: 10
(OSU: Ohio State University)
Abstract
Abstract Recent qualitative work on Therapeutic Communities (TCs) suggests that they help residents change by creating an environment that is simultaneously challenging and supportive. There is evidence that social networks that feature numerous closed triads are both more supportive and more likely to influence individual behavior. This implies that TC residents whose peer social networks include more closed triads should have improved outcomes. The social network in this study consists of the affirmations exchanged between 1312 men who resided at a 90 bed TC in a Midwestern state over a period of eight years and includes a total of 34,667 weighted edges. The network was analyzed using the Temporal Network Autocorrelation Model (TNAM) based semiparametric Cox model, thereby using a statistical methodology that accounts for dependence between individuals in the network. Participants whose social networks of TC peers included a higher percentage of closed triads were at a decreased hazard of reincarceration following termination when controlling for age, length of stay and the number of peers who eventually graduated who affirmed the residents. These results support the longstanding TC contention that the community as a whole is the method of clinical treatment. Further quantitative research into TC processes and outcomes should ideally include social network surveys and statistics in order to avoid biases associated with violations of statistical independence assumptions.
  • References (48)
  • Citations (1)
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References48
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ABSTRACTBackground: Therapeutic communities (TCs) maintain order and encourage behavior change through a system of peer corrections. This study is the first quantitative analysis of the dynamics of the exchange of peer corrections at TCs. Objectives: We applied longitudinal social network analysis to compare the reactions of TC residents to peer versus staff intervention, while analyzing dynamics of correction exchange among residents. Method: The data consisted of a large database of staff and ...
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Democratic Therapeutic Communities (TCs) are long-term group programs that address severely ingrained clinical populations. These psychodynamically informed social environments can facilitate improvement in people suffering from personality pathology. However, the TCs鈥 working principle is not well documented, which threatens its continued existence. To gain further insight into how TCs work, this study explores former TC residents鈥 perspectives on their treatment, its outcome, and the process o...
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Abstract Background Researchers have begun to consider the ways in which social networks influence therapeutic community (TC) treatment outcomes. However, there are few studies of the way in which the social networks of TC residents develop over the course of treatment. Methodology We used a Temporal Exponential Random Graph Model (TERGM) to analyze changes in social networks totaling 320,387 peer affirmations exchanged between residents in three correctional TCs, one of which serves men and two...
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#1Kimberly M. Davidson (PSU: Pennsylvania State University)
#2Jacob T. N. Young (ASU: Arizona State University)H-Index: 18
Abstract Given the high proportion of inmates with histories of substance use disorders in the era of mass incarceration, many correctional institutions have integrated the prison-based Therapeutic Community (TC) into their programming. While most evaluations provide evidence of lowered rates of relapse and recidivism, the majority of TC graduates still have negative outcomes post-release. Filling an important gap in the TC treatment literature, we analyze quantitative measures of treatment enga...
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