Bringing the Homefront to the Forefront: UK perspectives on critical research with military spouses
Abstract: On the 9th of July 2021, the Rethinking Military Spouses: Critical Research Group hosted a webinar titled ‘Bringing the Homefront to the Forefront: UK Perspectives on Critical Research with Military Spouses’. This report provides some information about the event and includes an overview of the main points of discussion focused on four key themes - access, methodologies, impact, and criticality. When conducting critical research with military spouses, researchers must consider a wide range of issues particular to this specific group. These include: • Challenges around gaining access and the implications that recruitment strategies might have on the voices that are heard. • Choosing a methodological approach and associated methods which enable rich perspectives and experiences to be heard – balancing the needs of research objectives whilst also considering possible benefits to the participants. • Balancing research objectives and activities with expectations of academic institutions, funders, and identified beneficiaries –considering what impact might be and how it can be achieved and accounted for. • Navigating how the dynamics of criticality must be balanced against the need to attract funding, articulate impact, and gain ethics approval from the MOD – with there sometimes being tensions between these factors. Research which centres military spouses is needed in order to understand the impact of military life, power, and processes beyond more obviously militarised phenomena.
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.