Influence of family factors on service members' decisions to leave the military
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Abstract: Service member retention is a crucial aspect in maintaining and advancing the U.S. military and its mission. To increase retention, it is important to understand why active duty personnel voluntarily leave while they are still highly qualified. For married service members, spouses likely influence the decision to stay or leave military service. The current study used data from the Millennium Cohort Family Study for 4,539 dyads comprising service members and their spouses to investigate family predictors of voluntary military separation. Multivariate mediation analyses were conducted to evaluate the role of military satisfaction (spouse and service member) and work–family conflict as mediators of the effects of both family life and military stressors on risk for military separation, while accounting for spouse and service member demographics. Results identified significant family factors operating through work–family conflict and military satisfaction that were associated with increased likelihood of service member voluntary separation, including number of children, spouse bothered by finances, and service member months away from home. Service members with spouses who reported higher levels of social support were significantly less likely to voluntarily separate, after operating through both work–family conflict and military satisfaction. Findings suggest that work–family conflict and military satisfaction play an important synergistic role in predicting the impact family and career factors have on voluntary separation. These modifiable factors may guide potential interventions to increase military retention efforts.
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.