Self-reorganization in transition from military to civilian life: Maria’s way
Qualitative research has demonstrated that transition from military to civilian life involves narrative identity reconstruction among service members. The reformulation of narrative identities may prove to become an existential quest for service members since the questions of who I am, where I am going, and what is my place in the world need to be (re)answered by the self in a new cultural context. Thus, a reorganization of stories also corresponds to a reorganization of I-positions in the self. This article presents the case study of Lieutenant Maria, one participant derived from a larger longitudinal research project designed to explore this process of transition, and aims to demonstrate new ways of understanding self-identity work in transition through a Dialogical Self Theory approach. The analysis of the case study suggests that self-reorganization was necessary for adaptation to a civilian cultural context that shaped alternate identities. Four types of factors were observed to have major influence upon the self-identity evolution: contextual promoters, a dialogical self-attitude, meta-cognitive activities, and a group of cooperative positions in the self which could evolve in a new context and through emerging identities.
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.