(Loss of) the super soldier: combat-injuries, body image and veterans’ romantic relationships
Abstract: Purpose: Some military personnel sustain physical injuries that alter their appearance, such as limb-loss and scarring. Veterans injured this way may experience body image distress and mental and physical wellbeing difficulties. While research with civilians indicates those with appearance-altering conditions may experience relationship difficulties, this issue remained unexplored among combat-injured veterans. This study aimed to understand how veterans who sustained appearance-altering combat injuries experienced and understood their changed appearance within the context of their romantic relationships. Materials and method: Semi-structured interviews with four male UK combat-injured veterans were conducted and analysed using Interpretative Phenomenological Analysis. Results: Three superordinate themes were generated: (loss of) the super solider; new states of vulnerability; and injury tests relationships. Conclusions: Masculinity was central to participants’ military identity and represented by their military bodies. Following injury and the loss of their military body, some experienced relationship challenges including a test to the foundations and commitment of their relationships. In contrast, some veterans’ relationships grew stronger, especially among participants who described dyadic coping. Additional challenges were related to decreased self-confidence, appearance concerns, and sex and intimacy. Implications for the provision of relationship support for combat-injured veterans and their partners through the long-term trajectory of rehabilitation 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.