The Stigma of Mental Health Problems in the Military
Abstract: The present review addresses the perceived stigma associated with admitting a mental health problem and seeking help for that problem in the military. Evidence regarding the public stigma associated with mental disorders is reviewed, indicating that the public generally holds negative stereotypes toward individuals with psychological problems, leading to potential discrimination toward these individuals. The internalization of these negative beliefs results in self-stigma, leading to reduced self-esteem and motivation to seek help. Even if soldiers form an intention to seek help for their psychological difficulty, barriers to mental health care may prevent the soldier from receiving the help they need. An overall model is proposed to illustrate how the stigma associated with psychological problems can prevent soldiers getting needed help for psychological difficulties and proposed interventions for reducing stigma in a civilian context are considered for military personnel.
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.