Mind the gap: Sex, Gender, and Intersectionality in military-to-civilian Transitions
Abstarct: The authors conducted a review of existing research on sex, gender, and intersectionality in relation to military-to-civilian transition (MCT). Extensive international studies and government resources, mostly from the United States, provide insight into the potential vulnerabilities and challenges encountered by historically under-represented military members and Veterans during MCT (i.e., by women, lesbian, gay, bisexual, transgender, and other sexual or gender minority, Black, Indigenous, and People of Colour military service members and Veterans). The reviewed sources also highlight government initiatives and tailored programs that exist internationally to address diverse Veteran needs. Canadian research and government initiatives on the topic are limited, and this gap needs to be kept in mind. To support equitable transition outcomes for all Veterans, research as well as policies, programs, and supports need to pay attention to sex and gender as well as intersecting factors such as sexuality, race, Indigeneity, and more.
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.