The Invisible Man: Male Veterans share their Lived Experience of Military Sexual Trauma in the British Armed Forces
Abstract: The overall aim of this study was to obtain detailed information that illustrated the lived experience of male veterans that had been victims/survivors of MSA & MST. As mentioned earlier, research regarding adult male sexual assault within a military setting is nonexistent in the UK with limited research available in the United States. The research that does exist focuses on prevalence rates and does little to describe the impact of MST on Male survivors, the differences between male and female survivors or potential treatment approaches. Research focused predominantly on female survivors suggests that MST is linked to a whole host of detrimental outcomes including increased rates of PTSD, mental/physical health problems, suicide attempts and decreased quality of life with a difficulty in adjusting to civilian life. In examining mental health diagnoses of all veterans with a positive MST screening, Kimerling found that women with a positive MST screening were most likely to be diagnosed with PTSD, dissociative disorders, eating disorders, and personality disorders, whereas men were most likely to be diagnosed with suicidal behaviour, personality disorders, PTSD, attention deficit hyperactivity disorder and military conduct problems, dissociative and bipolar disorders
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.