Abstract: What does it mean to be an academic who is also a war veteran? This paper examines that question as I delve into my own identity and positionality as a war veteran and as an academic who critically examines war and militarism. It is broken up into three sections: living war, writing war, and teaching war. Living war refers to what it is like to be a war veteran in academic spaces, from a student perspective to a teaching perspective. Writing war examines some of the ways in which war experiences can be utilized in academic writing, as it examines a few useful methodologies that were helpful and healing in my experience. Finally, teaching war reiterates the importance to centre war in the classroom and provides an example that I often use in the classroom. The primary aim of this paper is to discuss the reciprocal aspects of the interactions between my embodied war experience and higher education institutions.
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.