A mixed methods feasibility study of a virtual group-based social support program for older adults in residential care
Bibliographic record
Abstract
Objectives In response to calls for inventive ways to mitigate risks of physical distancing due to the COVID-19 pandemic for older adults living in residential care, the JAVA Music Club–Digital (JMC-D) was developed. The current feasibility study investigated benefits, usability, and implementation of weekly JMC-D sessions over 6 months.Method Employing a pre-post mixed methods study, depressive symptoms, loneliness, social isolation, and quality of life were measured at baseline, 3 and 6 months. Qualitative interviews were conducted at 3 months.Results Twenty-one residents were recruited. Across the three time points there was a large effect for depressive symptoms, social isolation, and quality of life, though not statistically significant. There was a significant immediate increase in happiness following engagement in the JMC-D sessions. Thematic analysis of semi-structured interviews generated two overarching themes: Experiencing the JMC-D (subthemes: Benefits, Navigating the virtual platform, Feedback) and Considerations for Implementation (subthemes: Perceived purpose, Characteristics that impact the experience, and Infrastructure and resources).Conclusion Findings are encouraging and suggest that the JMC-D may support emotional and other psychosocial indices of wellness in residential care during times of physical distancing. Appropriate staffing, resources, and internet accessibility are important for implementation and uptake.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".