Understanding resilience as it affects the transition from the UK Armed Forces to civilian life
Abstract: In the years following the release of the UK Ministry of Defence’s (MOD) Armed Forces Covenant1 and Strategy for Veterans, there has been growing interest among policy officials, charity representatives and academic experts in understanding the transition process for Service leavers. While recent evidence suggests that resilience is important to successful transition, no systematic review has been undertaken on the subject of UK Service leaver resilience and transition prior to this study. To address this research gap, RAND Europe was commissioned by the Forces in Mind Trust (FiMT) to undertake a literature review comprised of a systematic review of academic literature, a Rapid Evidence Assessment (REA) of academic and grey literature, and a scoping review of ongoing research on UK Service leaver resilience and transition. This study aims to improve understanding of whether, and if so how, resilience can affect transition pathways and outcomes for UK Service leavers.
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.