Exploring U.S. Veterans’ post-service employment experiences
Abstract: Although most U.S. veterans transition to civilian life successfully, securing employment and reintegrating into civilian communities, some veterans face transition challenges that can lead to or exacerbate mental and physical health problems. Emerging research from a survey conducted by Prudential indicates that difficulty transitioning to civilian life is largely attributable to employment (Prudential, 2012). This study sought to understand veterans’ employment experiences. Four focus groups (n = 33) with pre- and post-9/11 veterans who at the time were accessing housing and employment support services were conducted. Thematic analysis of focus group transcripts led to the emergence of 2 master themes: (a) organizational and societal barriers, such as limited availability of transition programs, discharge type, negative experiences of support services, and perceived discrimination; and (b) personal barriers, such as lack of initiative to plan and difficulty adjusting to working with civilians. Since data was collected for this study, updates to TAP have been implemented; this may have alleviated some of the reported barriers. The role of veterans’ personal characteristics in employment requires attention in the context of agency, initiative, identity, and cultural adjustment. Policy, programmatic, practice, and future research recommendations are made.
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.