“One of Us”: Reframed Labels, Compassion and Hope in Voluntary Prison Work With Ex-Servicemen
Abstract: Despite the growing body of literature on Prison Officers and therapeutic practitioners within correctional facilities, comparatively little research exists into prison volunteers. Using semi-structured interviews with caseworkers (n = 5), analyzed via Interpretative Phenomenological Analysis, this study explores the experience of being a Prison In-Reach Caseworker, supporting the male ex-Armed Forces population in Greater London prisons. Through identifying three superordinate themes of the inherent moral values of the shared past, compassion and “in” versus “out” of the prison system, the study concludes that the caseworkers, working outside the boundaries of the correctional system, reject the label of “criminal” and its associated consequences, choosing instead to attribute value and dignity to the prisoners, both as ex-Armed Forces personnel, and as human beings. The findings offer an insight into the consequences of positive labeling for perspectives of redemption and desistance and suggest the need for further investigation into the experiences and impact of prison volunteers working with different populations.
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.