Advancing an understanding of selves in transition: I-positions as an analytical tool
Self-identity work appears to be a challenge for many service members as they transition and reintegrate into civilian life. When other cultural influences seem to threaten an established self as it labors with transition, tension and conflict may arise and can potentially impact mental health. Insights from an ongoing longitudinal project on the subject matter indicate that an analysis of an individual, which utilizes the concept of I-positions may serve as a useful analytical tool during these processes. A longitudinal methodology combining a narrative approach with such an exploration of I-positions derived from a dialogical self framework may prove to be a promising avenue to advance the understanding of selves in transition beyond the dichotomy of the military and civilian spheres. The bridging capacity of I-positions lies partly in the capacity of significant others to link the self to both spheres and to help fill the perceived void between these two realms, which in reality may be overlapping and intertwined. The findings suggest, facilitated by two case study examples, that military transition to civilian life may benefit from a dialogical approach. This dialogical mind-set could even already be introduced and established during basic training. However, there is also a shared responsibility for individuals in civilian contexts to invite former service members into open dialogue just as the service members themselves shall strive to initiate earnest dialogue. Future research is encouraged to widen the methodology and knowledge of selves in 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.