Transitioning to Civilian Life: The Importance of Social Group Engagement and Identity Among Australian Defence Force Veterans
Abstract: Veterans transitioning to civilian life after leaving the military face unique health concerns. Although there is a significant body of research exploring veterans’ experiences of transition and predictors of well-being, there are limited studies examining how social group engagement influences veterans’ transition. We explored how Australian Defence Force veterans’ social group engagement and identity influenced their adjustment to civilian life and well-being. Forty Australian veterans (85% male; mean age = 37 years, range = 25–57 years) took part in in-depth, semi-structured interviews. Participants completed two mapping tasks (a social network map and life course map) that provided a visual component to the interviews. Interview transcripts were analysed thematically and interpreted by adopting a social identity approach. Joining the military involved a process of socialisation into military culture that for most participants led to the development of a military identity. An abrupt or difficult discharge from defence was often associated with a negative impact on social group engagement and well-being in civilian life. Veterans’ social group memberships may act not only as positive psychological resources during transition but also as a potential source of conflict, especially when trying to re-engage with civilian groups with different norms or beliefs. Military values inscribed within a veteran’s sense of self, including a strong sense of service, altruism and giving back to their community, may operate as positive resources and promote social group engagement. Engaging with supportive social groups can support transition to civilian life. Reintegration may be improved via effective linkage with programmes (e.g. volunteering, ex-service support organisations) that offer supportive social networks and draw upon veterans’ desire to give back to community. Social mapping tasks that visualise veterans’ social group structures may be useful for clinicians to explore the roles and conflicts associated with veterans’ social group memberships during transition.
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.