Caring for the carers: an evaluation of the recovery, readjustment and reintegration programme (R3P)
Abstract: The challenges faced by healthcare workers, not least during the response to the COVID-19 pandemic, have been extensively studied, and concerns continue to be highlighted in relation to their long-term mental health. Identifying the need to support their personnel, a leader-led structured programme of reflection: the recovery, readjustment and reintegration programme (R3P) was designed by the UK Defence Medical Services to mitigate the potential stressors associated with this outbreak and enhance the resilience of the workforce. 128 military personnel completed an evaluation of R3P. A survey included measures of anxiety before and after the intervention, perceptions of the discussion themes and whether these brought a sense of closure to areas of distress, and attitudes to help-seeking. Most respondents (86%–92%) rated the five discussion themes either ‘helpful’ or ‘very helpful’, 51% of respondents reported a sense of closure about an issue that had been causing distress and 72% of respondents felt better able to seek help should it be necessary. Evaluating the effect R3P had on anxiety, a Wilcoxon signed rank test elicited a statistically significant difference in anxiety pre-R3P and post-R3P; Z=−3.54, p<0.001. The median anxiety rating was 3.5 (IQR 4.75, 95% CI 1.25 to 6.00) before undertaking R3P, which decreased to 3 (IQR 4.75, 95% CI 1.00 to 5.75) after undertaking R3P. 39.1% of participants reported decreased anxiety, 18.8% reported increased anxiety and 42.2% reported no change. This evaluation has identified several positive aspects to R3P with many personnel reporting a reduction in anxiety, a sense of closure and increased likelihood of help-seeking. Several participants did report an increase in anxiety and the long-term impact of R3P on mental health and well-being is unclear. Further mixed-methods evaluation incorporating a longer follow-up is required.
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.