“A Hidden Community”: The Experiences of Help-Seeking and Receiving Mental Health Treatment in U.K. Women Veterans. A Qualitative Study
Abstract: Women veterans are often underserved in both the research into and provision of mental health treatment. This study explored women veterans’ experiences of mental health difficulties, help-seeking, and treatment provision. Semistructured telephone interviews with 19 U.K. women veterans who met criteria for posttraumatic stress disorder were conducted and Reflexive Thematic Analysis was used in analysis. Three superordinate themes encompassing participants’ experiences were developed: (a) attitudes toward mental health and help-seeking; (b) the need to acknowledge the uniqueness of women veterans; and (c) the structural elements of care provision. The findings indicate that women veterans have additional gender-specific challenges and needs concerning tailored pathways into help and support, as well as the environment and modality of treatment delivery, as distinct from veteran men.
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.