Making dialogue with an existential voice in transition from military to civilian life
Dialogical Self Theory has contributed to the endeavors to map and grid self-identity work in transition from military to civilian life throughout an empirical and longitudinal research project which focuses on existential dimensions. This article is based on a case study from this project and centers upon Sergeant Jonas, who, upon his return from deployment in Afghanistan, struggled with his transition as a new existential position was vocalized throughout the following annual interviews. This voice narrated feelings of meaninglessness, emptiness, and of having been deceived. In turn, this existential voice required an answer to a question which apparently had no answer. The meaning-making eventually evolved into an acceptance which enabled Jonas to proceed with his life. Dialogical processes between positions are important in order to go on with life amid existential concerns in the aftermath of military service since dialogicality of the self opens up a complex of dynamics of meaning-making processes, negotiations, and transformations. Based on the findings, it is suggested that the Personal Position Repertoire could potentially be strengthened by the addition of an internal existential position to its standard repertoire, at least when working with military personnel and/or veterans.
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.