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 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.
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