It's All Very Well for Politicians in Whitehall to Run a War, But They're Not on the Ground: UK Military Veterans' Experiences of Betrayal-Based Moral Injury
Abstract: Moral injury (MI) has recently gained traction in the literature on military veteran distress; however, research often fails to distinguish between two widely cited yet distinct definitions of MI. The two definitions conceptualize mainly perpetration-based experiences and betrayal-based experiences, which have been shown to have different outcomes. U.K. research has mainly conceptualized MI using a perpetration-based model, and it is unclear to what extent a betrayal-based model is relevant to this population. Therefore, through 15 interviews, this study aimed to explore how U.K. military veterans describe their moral beliefs and explore ways in which these were transgressed, in relation to their military service, through the conceptualization of betrayal-based MI. Utilizing reflexive thematic analysis, this article constructed two main themes in relation to our participants experiences of MI: (a) “what’s right”—a military moral compass and (b) betrayal of what’s right by leaders and systems. Through these themes, our analysis highlights the ways in which participants make sense of what they see as right, or moral, followed by an exploration of the ways in which this was transgressed or betrayed during their time in the military. The findings in this article demonstrate the usefulness of understanding U.K. military veterans’ experiences through the conceptual and analytical lens of betrayal-based MI. We conclude with the suggestions that future research should delineate between perpetration and betrayal-based MI to understand the complexities and nuances of experiences and that interventions would benefit from considering specific components of betrayal-based MI when working with military veterans.
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.