Exploring Barriers to Mental Health Treatment in the Female Veteran Population: A Qualitative Study
Abstract: Although female veterans are a growing population, there remains limited research on their unique experiences specifically within the United Kingdom (UK). The limited data available indicates that female veterans are at increased risk of developing mental health disorders following their discharge from the military. However, female veterans make up a small proportion of those seeking mental health treatment. This study is the first qualitative study to explore the barriers faced by UK female veterans in accessing mental health treatment. The sample of the present study took part in a larger cohort study investigating the mental health needs of female veterans. A total of 61 female veterans responded to a qualitative item on the online survey that was investigating barriers they experienced in accessing mental health treatment compared to their male peers. Responses were analysed using thematic analysis to identify key themes in the data. Five key themes were identified: access barriers, lack of understanding from professionals, gender-related discrimination, mental health stigma, and sexual orientation-related discrimination. The current findings suggested that in addition to treatment-seeking barriers experienced more generally in the military, female veterans may face unique barriers to seeking support. With little veteran research focusing solely on the needs of women, further research is needed to better understand the barriers women face in seeking support. Further attention is also required to ensure such findings are practically implemented within veteran-specific and general mental health services.
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.