Bridging the Gap? Ex-Military Personnel and Military–Civilian Transition Within the Prison Workforce
Abstract: Prior research into military–civilian transition has suggested that the Prison Service may be a popular destination for Armed Forces leavers, but the experience of former military personnel within the prison system as prison staff (rather than as Veterans in Custody) has so far been overlooked. As a result, we know very little about their route into prison work. This article reports on a UK study investigating the experience of prison personnel who have previously served in the military and presents the first set of empirical evidence addressing these critical questions. Whilst our findings mirror prevailing assumptions of a relatively seamless transition to post-military careers (and, in particular, those within Protective Service Occupations), few had intended a career in prison work specifically. Such trajectories may influence personal military–civilian transitions, as well as job performance in prison work and, by extension, the everyday lives of prisoners and other prison staff.
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.