Awareness of and willingness to access support among UK military personnel who reported a mental health difficulty
Abstract: Being aware of and willing to access mental health services are important first steps in help-seeking behaviour. However, evidence suggests that UK armed forces personnel are not always aware of or willing to access sources of mental health support. This study explored which sources of support UK armed forces personnel are most aware of, and willing to use, for a self-reported mental health problem and the possible differences between serving and ex-serving personnel. Data were taken from a cross-sectional study of 1,432 UK serving and ex-serving personnel who had self-reported a mental health, stress, or emotional problem in the past three years. Military personnel, irrespective of serving status, were most aware of, and willing to access, formal medical services. In contrast, there was a low awareness of and willingness to use ex-serving-specific support services among ex-serving personnel. Future service delivery and policy should focus on improving the variety of sources of support that ex-serving personnel are aware of, and willing to use, to enable them to make informed choices about where to seek help if needed.
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.