Evidence map›Paper›PMID 34914758›Full record

ArticlePloS one2021

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

Benjamin Campbell, Keith Warren, Mackenzie Weiler, George De Leon

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.4field-weighted citation impact, top 34% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 2 institutions in 1 country.

Benjamin CampbellDepartment of Political Science, The Ohio State University, Columbus, Ohio, United States of America.ORCID 0000-0003-2611-0024
Keith WarrenThe Ohio State University College of Social Work, Columbus, Ohio, United States of America.ORCID 0000-0003-1055-1749
Mackenzie WeilerDepartment of Political Science, The Ohio State University, Columbus, Ohio, United States of America.
George De LeonNew York University Rory Meyers College of Nursing, New York, New York, United States of America.
The Ohio State University · USNew York University · US

Funding

Development of a statistical framework for the analysis of social network predictors of therapeutic community outcomesR34DA043079 · NIDA · OHIO STATE UNIVERSITY · PI WARREN, KEITH LEVERETT · 2018 to 2020
$690k
NIDA NIH HHS R34 DA043079
6 · The paper itself

Abstract

introductionTherapeutic 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.

methodsData 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.

resultsResidents 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.

conclusionThese results suggest that TC residents move through a social network hierarchy and that movement through the hierarchy predicts successful graduation.

Indexed as

Databases, FactualFemaleForecastingFormative FeedbackHierarchy, SocialHumansLogistic ModelsMaleModels, TheoreticalPeer GroupSocial NetworkingSubstance Abuse Treatment CentersSubstance-Related DisordersTherapeutic CommunityTreatment Outcome

Identifiers

PMID34914758
PMCPMC8675758
OpenAlexW4200119391

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.