Military Commitment and Identity as Implicit Religion: A Key to Understanding the Loss of Profundity in the Transition from Military to Civilian Life
This article is based on a rare longitudinal interview study on the transition from military to civilian life in which participants shared their experiences over the course of ten years. The challenges of transition included disconnection from a collective life that had previously offered service members identity, community, camaraderie and purpose – experiences that can be understood as the partial loss of something profound. An abductive analysis was conducted which centred on the integrating foci of commitment and the creation of a military identity derived from implicit religion. When viewed through this lens, the significance or strength of implicit religion and belief, in the context of military commitment, can be described as paramount and sacred – worthy of dying for. The findings offer a novel understanding of the profound experiences related to military communal life, purpose, and identity during active service (that is, the conceptualization as implicit religion) as well as the loss of these implicit religious elements during the transition out of military life, and how the participants have thought about and dealt with this loss in various ways.
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.