Morally Injurious Experiences and Mental Health: The Moderating Role of Self-Compassion
Abstract: Introduction: Military veterans are at heightened risk for developing mental and behavioral health problems. Morally injurious combat experiences have recently gained empirical and clinical attention in negative outcomes observed in this population. Objective: Extending extant research, the current investigation assessed the relationship between morally injurious experiences and mental and behavioral health outcomes. Further, it examined the potential protective role of self-compassion in these relationships. Method: Participants included 222 military veterans (M age= 35.08, 77.30% male) who completed online questionnaires. Results: Analyses indicated that self-compassion significantly moderated the relationship between exposure to morally injurious experiences and posttraumatic stress disorder, depression severity, and deliberate self-harm versatility. Conclusions: These results highlight the potential clinical utility of self-compassion in military mental health, particularly in the context of morally injurious experiences.
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.