Experiences of ethnic minority personnel in the armed forces: A systematic review
Abstract: Ethnic minority personnel experience greater levels of harassment and discrimination than their nonminority counterparts. This review demonstrates the impact of these experiences on ethnic minority personnel in the armed forces. A literature search was conducted in PubMed, PsycInfo, PsycArticles, EBSCO, and Web of Science. Sixteen articles that discussed Black, Asian, and ethnic minority armed forces personnel were analyzed. Much of what is known about ethnic minority experiences of serving in the armed forces is based on ethnic minorities in the U.S. Armed Forces. The available literature shows that ethnic minority serving personnel and Veterans experience greater disadvantage than their native counterparts, both during and after service. Ethnic minority personnel reported poorer health than white personnel and fear of criticism from their ethnic minority community on disclosure of traumatic experiences. Ethnic minority personnel were also more likely to access formal mental health services yet less likely to engage in treatment, particularly women. Three themes were identified: cultural identity, health status and health utilization, and trauma and discrimination. Research reports often do not highlight individual ethnic minority groups, thus making it difficult to draw conclusions about them. Future research should consider evaluating the psychosocial context influencing functioning among different ethnic minority groups and should also explore the benefits of serving in the armed forces.
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.