Embracing Diverse Women Veteran Narratives: Intersectionality and Women Veteran’s Identity
Abstract: Women veterans are the fastest-growing population of veterans, yet women still face many barriers while serving and after leaving the military. An often-overlooked aspect in research and literature is how women develop their identity as veterans from their experiences of racism, sexism, heterosexism, classism, and other forms of oppression and discrimination while serving in the military and the invisibility or the lack of recognition as veterans after returning to civilian life. Few articles in the literature discuss intersectionality theory or framework in connection to military and women veterans’ experience or the role of identity formation as a veteran due to these experiences or how it impacts women veterans’ health outcomes. In this article, the role of institutional betrayal is explored as an additional barrier for women veterans as well as the intersectionality framework applied to the military as an institution. As the need for services for women veterans increase, understanding the impact of these intersections of identity and experiences of discrimination and oppression can be crucial in understanding the complexity of identifying as veterans and living in a society that does not see or value their experiences, as women or as veterans.
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.