Singing lessons as a path to well-being in later life
Bibliographic record
Abstract
Seventy-two persons, who had begun voice lessons after 40 years of age, were invited to complete an online survey that focused on the singers’ experience, motivation, goals, health and well-being, repertoire, practice, and demographic information; 48 respondents (33 females, mean age 60.81 years, range 48.83–82.08, SD = 6.99) completed the questionnaire. Most participants indicated that enjoyment and personal growth motivated their taking lessons. Over 90% commented on benefits of singing to their physical health (e.g., breathing) and mental health (e.g., mood, less depressive episodes). Despite the solitary aspect of singing lessons, 67% reported positive changes in social relations since taking lessons. Benefits to professional relations were also reported (e.g., confidence, listening to others). Repertoire level was generally high, consistent with a high average university educational level. Cost of lessons and time demands may account for the generally high socioeconomic status of respondents. Given that the singing voice is a musical instrument available to almost everyone, results might motivate older adults to consider taking voice lessons, encourage health care professionals to consider voice lessons as interventions to benefit clients, and persuade governments to subsidize voice lessons for older adults in their jurisdictions. The study provides a foundation for future research on the relative impacts on well-being of vocal lessons versus choral singing in the context of relative investments in the two activities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".