Unlocking student service members and veterans success: Welcoming student service members and Veterans and supporting student service members and veterans experiences
Abstract: Helping student service members and veterans (SSM/Vs) earn a college degree is central to supporting them post-service. Yet, this generation of SSM/Vs faces challenges in higher education, including problems adapting, poor health, and administrative constraints, contributing to worsened academic outcomes and a sense of isolation on campus. This monograph synthesizes research on the challenges facing SSM/Vs. It also frames common aspects of successful programs aiding SSM/Vs as three areas for intervention: how administrators and faculty can create a welcoming campus for SSM/Vs; ways universities can create support systems for SSM/V social, health, and academic success; and engaging community partnerships to enable these efforts. Whether SSM/Vs overcome their challenges and unlock their strengths is contingent on opportunities provided within the school itself, and by its faculty, administrators, students, and community. Central to these efforts should be the goal of inclusivity enhancement, community building, and reduction of health-related dysfunction among SSM/Vs.
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.