Implementing the Java Music Club in Residential Care: Impact on Cognitive and Psychosocial Health
Bibliographic record
Abstract
Background: 90% of long-term care (LTC) residents experience cognitive impairment. Social support may benefit cognition by decreasing depression and loneliness. Objective: To investigate the effects of the Java Music Club, a manualized social support program, on cognition and psychosocial health among LTC residents. Methods: The Java Music Club was implemented 1x/week for three months. Participants (n=24, 91.7% female) completed cognitive tasks and psychosocial questionnaires before (T1), after (T2), and three months following (T3) participation. Qualitative interviews to explore perceptions of the Java Music Club were conducted at T2 with participants and recreation coordinators. Results: Decreased loneliness from T1-T2 (t = 3.31, p = .003) and T2-T3 reductions in depressive symptoms (F = 3.459, p = .043) and subjective memory complaints (F = 3.837, p = .048). Qualitative interviews illustrated important group elements, and that the Java Music Club was enjoyable and promoted social engagement. Conclusions: Participation in the Java Music Club is a promising approach to counter loneliness, depressive symptoms and subjective memory complaints in LTC residents.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".