'Changing Step': The transition from the Regular Army to civilian life and work
Abstract: The British Army has always recognized that the majority of their full-time personnel will leave the Army and move into other employment sectors before their formal retirement from the world of work. In fact, the majority of Army personnel will work, on average, longer in other employment sectors than in the Army itself with, for example, about half of personnel serving six years or less in the Army, and with a current mandatory retirement age of 55 for most personnel. Therefore, there has been a long-standing interest in managing the transition from military to civilian life for those leaving the Army. This chapter will deal with the recent history of how the Army prepares its soldiers for leaving the Army, sets the context for why it does this, and describes how individuals are being encouraged to think of a ‘through-career’ transition to civilian life that emphasizes education, individual development, personal planning and preparation for life beyond the Army. The details of the actual provisions that members of the Army can access in preparation for leaving will also briefly be described along with some of the challenges that may arise in the future.
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.