Military adolescents’ experiences of change and discontinuity: Associations with psychosocial factors and school success
Abstract: Objective: Drawing from the contextual model of family stress, social support and depressive symptoms were examined as two psychosocial factors that may link experiences of change and discontinuity common to military families to military adolescents’ school success (i.e., academic achievement, school engagement, and homework commitment). Background: Many military adolescents experience frequent changes that create discontinuity (e.g., parental deployments, relocations) and can impact their school success. Research has not examined psychosocial factors as a possible mechanism explaining the link between family change and discontinuity and adolescents’ school success. Method: A path model based on 821 military adolescents’ responses examined how experiences of family discontinuity were associated with adolescents’ psychosocial factors and, in turn, their school success after accounting for grade level, sex, and racial/ethnic minority status. Indirect effects between family discontinuity and school success were also evaluated. Results: For adolescents attending public school off the military installation, parental deployment was significantly associated with less social support, and recent relocation was significantly associated with elevated depressive symptoms. Both psychosocial factors were associated with adolescents’ academic achievement, school engagement, and homework commitment. Implications: Prevention and intervention efforts directed at enhancing both social support and positive mental health are discussed at various systemic levels including families, schools, and communities.
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.