Impulsivity, trauma history, and interoceptive awareness contribute to completion of a criminal diversion substance use treatment program for women

Emily M Choquette; Katherine L Forthman; Namik Kirlic; Jennifer L Stewart; Mallory J Cannon; Elisabeth Akeman; Nick McMillan; Micah Mesker; Mimi Tarrasch; Rayus Kuplicki; Martin P Paulus; Robin L Aupperle

doi:10.3389/fpsyg.2024.1390199

Impulsivity, trauma history, and interoceptive awareness contribute to completion of a criminal diversion substance use treatment program for women

Front Psychol. 2024 Sep 4:15:1390199. doi: 10.3389/fpsyg.2024.1390199. eCollection 2024.

Authors

Emily M Choquette¹, Katherine L Forthman¹, Namik Kirlic^{1

2}, Jennifer L Stewart^{1

2}, Mallory J Cannon¹, Elisabeth Akeman¹, Nick McMillan³, Micah Mesker³, Mimi Tarrasch³, Rayus Kuplicki¹, Martin P Paulus^{1

2}, Robin L Aupperle^{1

2}

Affiliations

¹ Laureate Institute for Brain Research, Tulsa, OK, United States.
² Department of Community Medicine, University of Tulsa, Tulsa, OK, United States.
³ Women in Recovery, Family and Children's Services, Tulsa, OK, United States.

Abstract

Introduction: In the US, women are one of the fastest-growing segments of the prison population and more than a quarter of women in state prison are incarcerated for drug offenses. Substance use criminal diversion programs can be effective. It may be beneficial to identify individuals who are most likely to complete the program versus terminate early as this can provide information regarding who may need additional or unique programming to improve the likelihood of successful program completion. Prior research investigating prediction of success in these programs has primarily focused on demographic factors in male samples.

Methods: The current study used machine learning (ML) to examine other non-demographic factors related to the likelihood of completing a substance use criminal diversion program for women. A total of 179 women who were enrolled in a criminal diversion program consented and completed neuropsychological, self-report symptom measures, criminal history and demographic surveys at baseline. Model one entered 145 variables into a machine learning (ML) ensemble model, using repeated, nested cross-validation, predicting subsequent graduation versus termination from the program. An identical ML analysis was conducted for model two, in which 34 variables were entered, including the Women's Risk/Needs Assessment (WRNA).

Results: ML models were unable to predict graduation at an individual level better than chance (AUC = 0.59 [SE = 0.08] and 0.54 [SE = 0.13]). Post-hoc analyses indicated measures of impulsivity, trauma history, interoceptive awareness, employment/financial risk, housing safety, antisocial friends, anger/hostility, and WRNA total score and risk scores exhibited medium to large effect sizes in predicting treatment completion (p < 0.05; ds = 0.29 to 0.81).

Discussion: Results point towards the complexity involved in attempting to predict treatment completion at the individual level but also provide potential targets to inform future research aiming to reduce recidivism.

Keywords: machine learning; prison diversion program completion; substance abuse treatment; substance use treatment completion; women’s substance use.

Grants and funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. Funding was provided by the Laureate Institute for Brain Research. The funders had no role in the study design; data collection, analysis or interpretation of the data; in the writing of this report; or in the decision to submit the article for publication. All researchers are independent from the funders and had access to all of the data.