Existential spiritual life among Swedish service members in transition: Marking out trends.
This study investigates, adopting a longitudinal approach, existential spiritual life among military service members (N = 19) during their transition into civilian life. Four general trends within the sample are presented, and highlighted by the 4 case study examples that best capture each of the trends. An interpretation of the findings suggests that transition from active service requires some type of identity reconstruction among the majority of the sample, as the story of who I am is interrupted. Frequently, this initially has a negative impact on the existential spiritual life. The longitudinal outcomes of the identity work depend upon personal control, motivation and access to pathways that support growth of the existential spiritual life. Future research is encouraged that examines this hypothesis more broadly.
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.