Military Ethics and Well-being, a Soldier's Journey
Abstract: In light of emerging research on moral injuries, this article explores the impact of soldiers’ indoctrination on their identity after they have lived through the experience of war. The injuries that soldiers live and witness can act as a source of motivation to carry on in intensive combat, but are often internalized indefinitely, even after their return to the regiment. A discourse analysis will demonstrate the process by which the actors are able to overcome the moral combat and will discuss the conditions that are essential to efficient healing. Ultimately this article aims to illustrate how soldiers can free themselves from the reflexes and, more importantly, from the moral values that kept them alive on the battlefield but that are in contradiction with the moral values of the society in which they are trying to reintegrate themselves.
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.