Investigating the Moral Challenges Experienced by UK Service Police Veterans
Abstract: Previous research has explored the negative effects of exposure to potentially morally injurious events among armed forces veterans and active-duty military personnel generally. However, this current pilot research provides a unique contribution to the extant research literature by examining the specific moral challenges experienced by a potentially at-risk and under-researched sub-group of military personnel. Semi-structured interviews were conducted with 10 United Kingdom (UK) Service Police veterans to identify any moral challenges encountered during their military service and to investigate the experience of moral dissonance underlying these events. Using Interpretative Phenomenological Analysis (IPA), four main themes (with sub-themes) emerged from the data: (a) violation of a moral code, (b) experience of disillusionment, (c) attempted resolution of moral dissonance, and (d) risk and protective factors for moral dissonance. Evidence of the types of moral challenges encountered by Service Police veterans during their military service and the negative consequences of moral dissonance was explored for the first time. Some of these findings overlap with existing evidence from non-Service Police research, although novel insights were also identified, such as the attempts of Service Police veterans to resolve moral dissonance through acting with moral courage, self-preservation, or seeking acceptance. The current research therefore provides a rationale for further investigation into the experience of moral dissonance and impact of exposure to morally injurious events in this sub-population of veterans. Potential implications for advancing conceptual understanding of moral injury and informing interventions to prevent the development of morally injurious outcomes 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.