"Once a Soldier, a Soldier Forever": Exiled Zimbabwean Soldiers in South Africa
Abstract: Through military training, soldiers’ bodies are shaped and prepared for war and military-related duties. In the context these former Zimbabwean soldiers find themselves—that of desertion and ‘underground life’ in exile in South Africa—their military-trained bodies and military skills are their only resource. In this article, we explore the ways in which former soldiers maintain and ‘reuse’ their military-trained bodies in South Africa for survival, in a context of high unemployment and a violent, inner-city environment. We look at their social world and practices of soldiering—a term that refers to the specific forms of their social interaction in exile, through which they keep their memories of their military past alive. By attending to their subjectivities and the endurance of their masculine military identities and bodies, we aim to contribute to the discussion on demilitarization, which has largely focused on the failure of models of intervention to assist ex-combatants in postconflict contexts.
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.