The effects of participating in a community concert band on senior citizens’ quality of life, mental and physical health
Bibliographic record
Abstract
Music participation has been shown to have many positive effects on older adults, including on perceived health, mental well-being and social interactions. However, researchers have yet to explore the experience of older adults who are just starting their musical journey. This study’s goal was to determine the extent to which participating in a community band had an impact on the quality of life (QoL), mental health and physical health of beginner musicians 60+ years old. The theoretical framework used for this project was the biopsychosocial model, which posits that health is influenced by the interactions between biological, psychological and social factors. Using a quasi-experimental design, the researchers followed eight participants over four months of music instruction and compared them to a control group of eight non-musicians. Interviews, questionnaires and physiological tests were carried out pre- and post-intervention. Results were analysed using the biopsychosocial model’s factors and thematic analyses. Physically, subjects reported self-perceived improvements in breathing and physical endurance. Psychologically, benefits included increased well-being, cognitive stimulation, sense of purpose and identity, as well as overall enjoyment. Socially, positive outcomes identified were being part of a group, meeting new people, and keeping in touch or reconnecting with friends. Band members reported a high level of satisfaction, which is in keeping with findings from the literature. Further pedagogical considerations are discussed.
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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 | 0.002 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".