A developmental formative evaluation of a pilot participatory music program for veterans with housing insecurity
Abstract: Interventions are needed to improve well-being and promote community reintegration among Veterans with housing insecurity. The objective was to conduct a developmental formative evaluation of a participatory music program. This single-site, pilot study implemented a participatory music program at a U.S. Department of Veterans Affairs (VA) Homeless Domiciliary that included one-hour sessions (group music instruction and ensemble playing), 3 times per week for 3 months. Intervention development was guided by the Model of Human Occupation (MOHO). Evaluation was guided by the MOHO and the Consolidated Framework for Implementation Evaluation (CFIR). Qualitative data were collected via semi-structured interviews from participants and non-participants, and were analyzed using an interdisciplinary, constant comparison qualitative analysis technique. Sixteen program participants and 8 non-participants were enrolled, age range 26–59 (mean 41; standard deviation, 11) years; 75% were White. The sample for this study (N = 12) included five participants and seven non-participants. Semi-structured interview responses produced three salient themes illuminating Veterans’ perspectives: (1) key characteristics of the intervention (the relative advantage of the participatory program over other problem-focused programs; the importance of a supportive, encouraging teaching; the group setting; the role of music); (2) the therapeutic power of the program (based on it being enjoyable; and serving as an escape from preoccupations); and (3) the context and culture (which included Veterans supporting each other and the Domiciliary setting). Veterans described the benefits of a participatory music intervention compared to problem-based groups, which included enjoyment, skill acquisition facilitating pride, escape, reconnecting with their identity prior to current problems, and experiencing positive aspects of Veteran culture such as mutual support and discipline. These data support ongoing research about participatory music programs to support Veterans with housing insecurity.
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.