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Semantic Network Analysis of Affirmations and Corrections Given in Three Therapeutic Communities: Does Content Predict Outcomes?

Doogan, Nathan J
http://rave.ohiolink.edu/etdc/view?acc_num=osu1396347560

Abstract Details

2014, Doctor of Philosophy, Ohio State University, Social Work.
Background: This study is partially a reaction to a large and expensive U.S. prison system with excessive reincarceration rates. It is also a response to a need for a deeper understanding of the mechanisms driving effectiveness of a substance abuse treatment modality known as the therapeutic community (TC) for substance abusing offenders. The TCs studied in this research are prison step-down and diversion programs in which peer support and guidance are principal components aimed at the antisocial root of substance abuse and crime. The community is the key therapeutic element. Specifically, and among other interactions, residents are expected to affirm and correct their peers who benefit or cause detriment to the community. Data and Methods: These interactions and a summary of their content in textual form are archived as a normal function of the TC processes, and are the content data studied in the present research. The first stage of the study clusters residents based on the words they have used in the affirmations and corrections given to others. The average reincarceration rate of each cluster of residents is estimated with a Cox proportional hazards model of time until reincarceration. In the second stage of the study, two word maps are inferred from the content produced by the residents of each cluster—one for residents’ first half of treatment, and one for the second half. Words in the maps are connected if they are used together in individual messages more often than would be expected by chance under a well defined and sensible null model. The null model is sufficiently complex that an analytical solution is not available. Thus, this study advances existing techniques by using Monte Carlo methods to numerically calculate the necessary values. Changes between the time one and two inferred word maps are thoughtfully characterized using a set of network statistics from the field of social network analysis. Resident clusters are then compared with a meta regression model that accounts for the precision of the reincarceration hazard estimates calculated in the first stage of the analysis. The model regresses the reincarceration hazard of resident clusters on word map change statistics and other controls. Results and Implications: Results suggest that residents adding more word connections, removing fewer word connections, and reducing their level of word clustering while in the TC do better at follow-up. These results seem to fit well with TC theory and existing TC empirical research. They also make sense from a learning perspective. That is, those residents making more connections among the words they use, presumably meaning they make more connections among the concepts they use to organize their world (i.e., they better integrate ideas) are the more successful residents post- release. The implications of these findings suggest adapting known learning theoretic teaching practices to TC programs for substance abusing offenders. Implications of the developed techniques for quantitatively analyzing textual data are also discussed as a useful tool for the field of social work where qualitative data is common.
Keith Warren, Ph.D. (Committee Chair)
Mo Yee Lee, Ph.D. (Committee Member)
Gregoire Thomas, Ph.D. (Committee Member)
Anderson Betty Lise, Ph.D. (Committee Member)
300 p.
Criminology; Social Work; Therapy
Therapeutic Community; Residential Treatment; Text Analysis; Network; Quantitative;

Recommended Citations

Citations

  • Doogan, N. J. (2014). Semantic Network Analysis of Affirmations and Corrections Given in Three Therapeutic Communities: Does Content Predict Outcomes? [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1396347560

    APA Style (7th edition)

  • Doogan, Nathan. Semantic Network Analysis of Affirmations and Corrections Given in Three Therapeutic Communities: Does Content Predict Outcomes? 2014. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1396347560.

    MLA Style (8th edition)

  • Doogan, Nathan. "Semantic Network Analysis of Affirmations and Corrections Given in Three Therapeutic Communities: Does Content Predict Outcomes?" Doctoral dissertation, Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1396347560

    Chicago Manual of Style (17th edition)

osu1396347560
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This open access ETD is published by The Ohio State University and OhioLINK.