The subjective underemployment experience of post-9/11 veterans after transition to civilian work
Abstract: BACKGROUND: Underemployment is a challenge for the civilian workforce and a particular risk for veterans as they transition from military service to civilian employment. Workers’ economic and demographic characteristics factor into underemployment risk. Veterans may be at greater risk due to specific economic and demographic factors, transitional factors (e.g., geographic relocation), and characteristics of their military service (e.g., military skill alignment with civilian jobs). OBJECTIVES: Describe underemployment experiences in employed post-9/11 veterans three years after their military transition to the civilian workforce. METHODS: The current study uses self-reported underemployment experience data from a longitudinal study of transitioning veterans. This study compares average perceptions of veteran underemployment experiences by specific groups (e.g., by race, gender, and paygrade) using analysis of variance and logistic regression. RESULTS: Veterans reported underemployment in their current jobs based on a perceived mismatch between the skills, education, and/or leadership experience they gained during military service. CONCLUSIONS: Veterans who were enlisted rank, identified as non-White, completed a bachelor’s degree, and indicated PTSD symptoms reported higher pervasive underemployment. Intervention implications for the results, such as employer and veteran employment supports, are discussed.
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.