‘He messaged me the other night and said you are my saviour’: An interpretative phenomenological analysis of intimate partners’ roles in supporting Veterans with mental health difficulties
Abstract: Introduction: The limited research base regarding Veteran welfare has emphasized the adverse psychosocial aspects of being the intimate partner of a Veteran struggling with mental health diffi culties. Despite this, previous research has identified that remaining in a romantic relationship can be a protective factor against mental health diffi culties. This study aims to explore intimate partners’ views of the role they play in supporting Veterans with mental health difficulties and the personal meanings they associate with this role. Methods: Six female partners of male Veterans were recruited using purposive sampling. Qualitative data were collected using semi-structured one-on-one interviews. Interpretative phenomenological analysis was used to gain an in-depth understanding of the lived experiences of partners of Veterans living with mental health difficulties. Results: Three superordinate themes were identifi ed: 1) the multi-faceted nature of support, 2) vicarious psychosocial consequences of the caring role, and 3) reconstruction of a Veteran’s identity after transition. Discussion: Intimate partners of Veterans described how they supported Veterans experiencing mental health difficulties, as well as detailing the challenges they faced. Future research topics are considered, and recommendations for further support for intimate partners are outlined.
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.