Canadian Armed Forces Transition Group: Leading the Way for a Smooth Transition
Abstract: Transitioning out of the military can be a difficult time for many veterans and can be especially challenging for members who are ill and/or injured. The Canadian Armed Forces Transition Group (CAF TG) Satisfaction Survey was administered to ill and/or injured Canadian Armed Forces (CAF) members who had accessed the services of their local Transition Centre (TC) over a two-year period. At the request of senior leadership in the CAF TG, an infographic was subsequently created to provide CAF members with an overview of some of the key findings. While a full report on the survey methods and results is planned for the near future, the purpose of this paper is to make this infographic and a short narrative more accessible to a broader audience. 749 CAF members completed the survey yielding a response rate of 32%. Nearly three-quarters of respondents reported being satisfied overall with their local TC, and only 11% reported dissatisfaction. In line with this finding, respondents reported that their well-being had significantly increased since accessing TC programs and services. Of the nearly 50% of respondents who reported that they were transitioning out of the CAF, most were aware of the transition services available. Finally, the majority reported being satisfied with the transition services they had used, in that they rated these as relevant, complete, timely, and helpful in preparing them for their transition from the CAF to civilian life. Together, these results demonstrate the value and importance of the programs and services offered by the CAF TG and TCs in providing military members with a smooth transition out of the CAF.
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.