A Symbolic Interactionist Perspective on the Divide within the Veteran Self
This article focuses a theoretical lens on the veteran self and discusses what this can mean for veterans, their loved ones, and society. Mead’s (1934) generalized other, Cooley’s (1902) looking-glass self, and James’s (1890) and Mead’s division of the self into the I and the Me are central concepts in this discussion. The article embraces a symbolic interactionist understanding, which leads to the suggestion that there is no deeper symbolic consensus between the civilian and military lifeworlds. Military symbols are not shared with and are not meaningful to civilians and therefore are not symbols at all in the civilian lifeworld. The rupture of the veteran self is due to the lack of shared symbolism with the self and civilian society at large. This creates a divide within the veteran self, which is hard to bridge. The article is written with a special address to the deployed veteran.
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.