Intimate partner violence among lesbian, gay, and bisexual veterans
Abstract: The present study describes intimate partner violence (IPV) perpetration and victimization alongside theoretically associated variables in a sample of lesbian, gay, and bisexual veterans. We conducted bivariate analyses (chi-square tests and independent t test) to examine whether the frequencies of IPV perpetration and victimization varied by demographic characteristics, military sexual trauma, alcohol use, and mental health symptoms. Out of the 69 lesbian, gay, and bisexual (LGB) veterans who answered the questions on IPV, 16 (23.2%) reported some form of IPV victimization in the past year, and 38 (55.1%) reported past-year perpetration. Among the 43 veterans who reported psychological IPV, roughly half (48.9%) reported bidirectional psychological IPV, 39.5% reported perpetration only, and 11.6% reported victimization only. LGB veterans who reported bidirectional psychological IPV in their relationships were younger and reported greater symptoms of posttraumatic stress disorder symptoms and depression. The results presented here call for universal screening of IPV perpetration and victimization to both accurately assess and ultimately intervene among all veterans. Inclusive interventions are needed for all genders and sexual orientations, specifically interventions that do not adhere to gendered assumptions of perpetrators and victims. (PsycInfo Database Record (c) 2023 APA, all rights reserved)
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.