Exploring the importance of predisposing, enabling, and need factors for promoting Veteran engagement in mental health therapy for post-traumatic stress: A multiple methods study

Abstract: Purpose: This study explored Veteran and family member perspectives on factors that drive post-traumatic stress disorder (PTSD) therapy engagement within constructs of the Andersen model of behavioral health service utilization. Despite efforts by the Department of Veterans Affairs (VA) to increase mental health care access, the proportion of Veterans with PTSD who engage in PTSD therapy remains low. Support for therapy from family members and friends could improve Veteran therapy use. Methods: We applied a multiple methods approach using data from VA administrative data and semi-structured individual interviews with Veterans and their support partners who applied to the VA Caregiver Support Program. We integrated findings from a machine learning analysis of quantitative data with findings from a qualitative analysis of the semi-structured interviews. Results: In quantitative models, Veteran medical need for health care use most influenced treatment initiation and retention. However, qualitative data suggested mental health symptoms combined with positive Veteran and support partner treatment attitudes motivated treatment engagement. Veterans indicated their motivation to seek treatment increased when family members perceived treatment to be of high value. Veterans who experienced poor continuity of VA care, group, and virtual treatment modalities expressed less care satisfaction. Prior marital therapy use emerged as a potentially new facilitator of PTSD treatment engagement that warrants more exploration. Conclusions: Our multiple methods findings represent Veteran and support partner perspectives and show that amid Veteran and organizational barriers to care, attitudes and support of family members and friends still matter. Family-oriented services and intervention could be a gateway to increase Veteran PTSD therapy engagement.

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.