Eigenvector centrality defines hierarchy and predicts graduation in therapeutic community units.


Journal

PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081

Informations de publication

Date de publication:
2021
Historique:
received: 06 02 2021
accepted: 01 12 2021
entrez: 16 12 2021
pubmed: 17 12 2021
medline: 27 1 2022
Statut: epublish

Résumé

Therapeutic communities (TCs) are mutual aid based residential programs for the treatment of substance abuse and criminal behavior. While it is expected that residents will provide feedback to peers, there has been no social network study of the hierarchy through which feedback flows. Data for this study was drawn from clinical records of peer corrections exchanged between TC residents in six units kept over periods of less than two to over eight years. Four of the units served men while two served women. Hierarchy position was measured using eigenvector centrality, on the assumption that residents who were more central in the network of corrections were lower in the hierarchy. It was hypothesized that residents would rise in the hierarchy over time. This was tested using Wilcoxon paired samples tests comparing the mean and maximum eigenvector centrality for time in treatment with those in the last month of treatment. It was also hypothesized that residents who rose higher in the hierarchy were more likely to graduate, the outcome of primary interest. Logistic regression was used to test hierarchy position as a predictor of graduation, controlling for age, race, risk of recidivism as measured by the Level of Services Inventory-Revised (LSI-R) and days spent in the program. Residents averaged a statistically significantly lower eigenvector centrality in the last month in all units, indicating a rise in the hierarchy over time. Residents with lower maximum and average eigenvector centrality both over the length of treatment and in the last month of treatment were more likely to graduate in four of the six units, those with lower maximum and average eigenvector centrality in the last month but not over the length of treatment were more likely to graduate in one of the six units, while eigenvector centrality did not predict graduation in one unit. However, this last unit was much smaller than the others, which may have influenced the results. These results suggest that TC residents move through a social network hierarchy and that movement through the hierarchy predicts successful graduation.

Identifiants

pubmed: 34914758
doi: 10.1371/journal.pone.0261405
pii: PONE-D-21-04132
pmc: PMC8675758
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Pagination

e0261405

Subventions

Organisme : NIDA NIH HHS
ID : R34 DA043079
Pays : United States

Déclaration de conflit d'intérêts

The authors have declared that no competing interests exist.

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Auteurs

Benjamin Campbell (B)

Department of Political Science, The Ohio State University, Columbus, Ohio, United States of America.

Keith Warren (K)

The Ohio State University College of Social Work, Columbus, Ohio, United States of America.

Mackenzie Weiler (M)

Department of Political Science, The Ohio State University, Columbus, Ohio, United States of America.

George De Leon (G)

New York University Rory Meyers College of Nursing, New York, New York, United States of America.

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