Narratives of chronic pain from armed forces veterans
This PhD thesis explores the experiences of 14 veterans of the UK Armed Forces who live with chronic pain. While there is a wealth of data exploring chronic pain in Armed Forces veterans, the existing literature overwhelmingly views chronic pain through a quantitative lens which does not explore the depth of chronic pain lived experience as a result of this; therefore, it is unclear how veterans live with and manage their chronic pain. Participants' stories of living with chronic pain were collected and analysed using dialogical narrative analysis and the analytical lens of autobiographical time, to detail veterans’ autobiographical stories of living with chronic pain, and how they use these stories to make sense of their pain experience in the context of their identity as a veteran. The analysis identifies that the participants understood and made sense of their chronic pain experience through three distinct storytelling phases. The first of these were stories of the military body, and this situated a career that was marked by physicality and the churn of life within the military institution. The second phase of storytelling was about the stigmatised body. These stories situate the attitudes toward pain and weakness within the military and the consequences of showing that the military body is infallible, which in turn inform stoic attitudes toward pain and personal pain management. The final phases of storytelling were told about the moving body, stories about the moving body were about making sense of a new body with pain, how some movement is avoided, and the consequences of this on identity. And how movement is used to understand, learn, and respond to and from their bodies on any given day. This is the first in-depth qualitative study that explores the lived experiences of veterans with chronic pain. It found that stories about chronic pain were told in the context of military experience and how the culture of the military shapes attitudes toward chronic pain, while movement was a keyway in which participants live with and manage their chronic pain.
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.