Cognitive fusion and post-trauma functioning in veterans : Examining the mediating roles of emotion dysregulation
Abstract: When cognitively fused, people have difficulty accepting and clearly perceiving their internal experiences. Following trauma, emotional non-acceptance and emotional non-clarity have been associated with post-trauma functioning. The aim of the present study was to integrate theory and research on cognitive fusion and post-trauma functioning to evaluate a theory-based model in which emotion dysregulation—specifically, emotional non-acceptance and emotional non-clarity—mediated the association between cognitive fusion and post-trauma functioning in a veteran sample. Participants were 149 veterans with a history of military-related trauma. Veterans completed measures of cognitive fusion, emotion dysregulation, posttraumatic stress disorder (PTSD) symptoms, and life satisfaction. Overall, emotion dysregulation and PTSD symptoms mediated the fusion-post-trauma functioning association in theoretically consistent ways. More specifically, fusion was related to PTSD through emotional non-clarity and fusion was related to goal dysregulation through emotional non-acceptance and PTSD. Our findings indicate that fusion impacts different aspects of post-trauma functioning through different mediators. How these different pathways could impact clinical decision making are discussed.
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.