Keith Warren1, Benjamin Campbell2, Skyler Cranmer2. 1. College of Social Work, The Ohio State University, Columbus, Ohio. 2. Department of Political Science, The Ohio State University, Columbus, Ohio.
Abstract
OBJECTIVE: Clustering, the tendency of individuals to form closed triads, is ubiquitous in human social networks. Previous research has found that therapeutic community (TC) residents whose social networks include a high degree of clustering are less likely to be reincarcerated following discharge. In this study, we test this finding in a larger number of TCs. METHOD: We use a temporal network autocorrelation model (TNAM) to analyze clustering in social networks of affirmations exchanged between TC residents as a predictor of the hazard of reincarceration. The networks were drawn from three corrections-based TCs, two of which include both men's and women's units and one of which housed only men. RESULTS: The findings were inconsistent across facilities. Increased clustering correlates with a reduced hazard of reincarceration for women at both facilities (β = -3.274, 95% CI [-4.299, -2.238]; β = -18.233, 95% CI [-32.370, -4.095]) and for men at two of the facilities (β =-0.910, 95% CI [-1.213, -0.606]; β = -1.393, 95% CI [-1.825, -0.961]). However, clustering increased the hazard of reincarceration for men at one facility (β = 5.558, 95% CI [4.124, 6.993]). CONCLUSIONS: These results support the idea that the likelihood of reincarceration following discharge from a TC is predicted by clustering, a network structure that occurs at a system level between the individual resident and the entire community. Inconsistency in the direction of the relationship suggests that future research should analyze predictors of prosocial clustering in TCs.
OBJECTIVE: Clustering, the tendency of individuals to form closed triads, is ubiquitous in human social networks. Previous research has found that therapeutic community (TC) residents whose social networks include a high degree of clustering are less likely to be reincarcerated following discharge. In this study, we test this finding in a larger number of TCs. METHOD: We use a temporal network autocorrelation model (TNAM) to analyze clustering in social networks of affirmations exchanged between TC residents as a predictor of the hazard of reincarceration. The networks were drawn from three corrections-based TCs, two of which include both men's and women's units and one of which housed only men. RESULTS: The findings were inconsistent across facilities. Increased clustering correlates with a reduced hazard of reincarceration for women at both facilities (β = -3.274, 95% CI [-4.299, -2.238]; β = -18.233, 95% CI [-32.370, -4.095]) and for men at two of the facilities (β =-0.910, 95% CI [-1.213, -0.606]; β = -1.393, 95% CI [-1.825, -0.961]). However, clustering increased the hazard of reincarceration for men at one facility (β = 5.558, 95% CI [4.124, 6.993]). CONCLUSIONS: These results support the idea that the likelihood of reincarceration following discharge from a TC is predicted by clustering, a network structure that occurs at a system level between the individual resident and the entire community. Inconsistency in the direction of the relationship suggests that future research should analyze predictors of prosocial clustering in TCs.
Authors: Keith Warren; Benjamin Campbell; Skyler Cranmer; George De Leon; Nathan Doogan; Mackenzie Weiler; Fiona Doherty Journal: Drug Alcohol Depend Date: 2019-11-26 Impact factor: 4.492