Perceptions of family acceptance into the military community among U.S. LGBT service members: A mixed-methods study
Abstract: Introduction: Despite calls to increase representation of diverse family structures in military family research, little is known about the experiences of the families of lesbian, gay, bisexual, or transgender (LGBT) service members (SMs). Using minority stress theory and a mixed-methods design, this study considers LGBT SMs’ perceptions of family acceptance within the military community. Methods: Survey data from 115 LGBT SMs who have a spouse or partner, a child or children, or both and qualitative data from 42 LGBT SMs who participated in semi-structured interviews were used. Demographic information, perceived family acceptance by the SM’s unit, leadership, and duty station, and beliefs about the appropriateness of military services for LGBT families were examined. Results: Many LGBT SMs, in both quantitative and qualitative findings, felt their families were accepted, although many still perceived a lack of acceptance, particularly regarding appropriateness of military family support services. No differences in perceived family acceptance were noted across sexual and gender identity categories. LGBT SMs who reported lower acceptance were more likely to report concerns about their family’s safety and the appropriateness of family support services, as well as increased physical and mental health symptoms. Discussion: These findings shed light on the experiences of LGBT military families and highlight both successes, with respect to inclusion, and areas for more scrutiny. Results raise particular concerns about supportive services that are perceived to be inappropriate for LGBT families. Evaluating LGBT families’ use of supportive services, barriers to accessing services, and outcomes of these experiences should be prioritized.
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.