Dialectical behavior therapy for justice-involved veterans (DBT-J): Feasibility and acceptability

Codebeautify.org Text to HTML Converter

Abstract: Justice-involved veterans are a high-risk, high-need subgroup serviced by behavioral health services within the Veterans Health Administration (VHA) system. Justice-involved veterans often have complex mental health and substance use difficulties, a myriad of case management needs, and a range of criminogenic needs that are difficult to treat with traditional outpatient VHA services. The present study represents an initial evaluation of dialectical behavior therapy for justice-involved veterans (DBT-J), a novel psychotherapy program providing 16 weeks of skills-based group therapy and individualized case management services to veterans with current or recent involvement with the criminal justice system. A total of 13 veterans were successfully enrolled into this initial acceptability and feasibility trial. Results broadly suggested DBT-J to be characterized by high ease of implementation, successful recruitment efforts, strong participant attendance and retention, high treatment fidelity, and high acceptability by veteran participants, DBT-J providers, and adjunctive care providers alike. Although continued research using comparison conditions is necessary, veterans who completed participation in DBT-J tended to show reductions in criminogenic risk across the course of treatment. Cumulatively, these findings suggest DBT-J holds potential as a VHA-based intervention to address the various needs of justice-involved veterans. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Read the full article
Report a problem with this article

Related articles

  • More for Researchers

    Identifying opioid relapse during COVID-19 using natural language processing of nationwide Veterans Health Administration electronic medical record data

    Abstract: Novel and automated means of opioid use and relapse risk detection are needed. Unstructured electronic medical record data, including written progress notes, can be mined for clinically relevant information, including the presence of substance use and relapse-critical markers of risk and recovery from opioid use disorder (OUD). In this study, we used natural language processing (NLP) to automate the extraction of opioid relapses, and the timing of these occurrences, from veteran patients' electronic medical record. We then demonstrated the utility of our NLP tool via analysis of pre-/post-COVID-19 opioid relapse trends among veterans with OUD. For this demonstration, we analyzed data from 107,606 veterans OUD enrolled in Veterans Health Administration, comparing a pandemic-exposed cohort (n = 53,803; January 2019-March 2021) to a matched prepandemic cohort (n = 53,803; October 2017-December 2019). The recall of our NLP tool was 75% and our precision was 94%, demonstrating moderate sensitivity and excellent specificity. Using the NLP tool, we found that the odds of opioid relapse postpandemic onset were proportionally higher compared to prepandemic trends, despite patients having fewer mental health encounters from which to derive instances of relapse postpandemic onset. In this research application of the tool, and as hypothesized, we found that opioid relapse risk was elevated postpandemic. The application of NLP Methods: to identify and monitor relapse risk holds promise for future surveillance, risk prevention, and clinical outcome research.