Ex-military personnel's experiences of loneliness and social isolation from discharge, through transition, to the present day
Abstract: Objectives: This study aimed to examine the unique factors of loneliness and social isolation within the ex-military population from discharge, through transition, to the present day. Design: A qualitative, Phenomenological approach was adopted. Methods: In-depth semi-structured interviews were carried out with 11 participants who had all served in the British Armed Forces and represented all three military services (Royal Navy; Army; Royal Air Force). Reflexive Thematic Analysis was used to analyse the data. Results: Three themes were generated-a sense of loss; difficulty in connecting in civilian life; and seeking out familiarity. The findings of this study were examined through the lenses of the Social Needs Approach and the Cognitive Discrepancy Model. Conclusions: Individuals developed close bonds in the military through meaningful and prolonged contact, reducing feelings of loneliness and social isolation during their time in service. The sense of belonging was key to social connection, but transition out of the military severed existing relationships, and a lack of belonging hindered the development of relationships within the civilian community. This study has implications for service provision relating to ex-military personnel and future service leavers.
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.