Abstract: The 2016 Army Families Federation (AFF) How we make work 'work' Survey, which gathers evidence from Army spouses in employment, to understand further how people are able to maintain employment despite the challenges faced as an Army spouse. Families often tell AFF about the barriers they face when seeking employment, but AFF was keen to hear from the many spouses who are making work ‘work’ for them and their family. AFF conducted a survey in September 2016 to gain a greater understanding of Army spouses and partners’ reasons for working and the secret to their success. There were 657 responses from spouses/partners in employment. The majority of working spouses/partners who took our survey feel working is worthwhile – not just for financial reasons but also for a sense of identity and independence. However, they do face some significant challenges. Support from the soldier and access to appropriate childcare are key determining factors in ensuring the spouse/partner is not disadvantaged in upholding employment. Whilst flexible working is available to some soldiers, AFF urges the MOD and Army to promote the policy more widely and improve command sensitivity to the growing trend of fair division of childcare responsibilities in a modern family.
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.