Re-conceptualizing music education in the older adult life course: A qualitative meta-synthesis
Bibliographic record
Abstract
The Seoul Agenda by UNESCO has set goals to develop arts education, ensuring that learners from all social backgrounds have lifelong access to arts education in a wide range of community and institutional settings. However, the purpose of lifelong learning for individuals beyond labor-market age has been largely overlooked, making it challenging to convince institutions, funders, and policymakers of its worth. The value accorded to the complex forms of lifelong learning in later life and the widely recognized health impacts of music on aging body and brain are the principal considerations to take into account when studying the effects of music education on older adults. In this study, we address the state-of-the-art research concerning older adults and music education in studies published in major peer-reviewed music education journals since the Seoul Agenda by UNESCO. We present the findings from a systematic literature review, followed by a qualitative meta-synthesis, focusing on the values, beliefs, and key concepts conveyed in the included studies. The findings of this study indicate that older adults are often portrayed narrowly and stereotypically, corroborating the issues in the sociology of aging. Our study highlights insights into the conceptualizations of music learning and participation in later life course and what these might mean for the policy and practice of later-life music education and the educational opportunities for older adults more broadly.
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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 | 0.090 | 0.146 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".