Aborted Transition Between Two Dichotomous Cultures as Seen Through Dialogical Self Theory
The purpose of this article is to further advance the understanding of self-identity work amid transition from military to civilian life, with emphasis on the complexities between and within the military and civilian cultural I-positions of a dialogical self. An analysis of a longitudinal case study of an aborted transition leads to the hypothesis that a culturally dominant military I-position that sustains a cultural dichotomy may hinder dialogical advancement toward reintegration into civilian life. The insights from this article can be used to better understand self-identity issues amid transition and may also have relevance for nonmilitary persons who are exposed to cultural transitions.
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.