The impact of group singing on children's subjective well-being: Mixed methods research
Bibliographic record
Abstract
Improving the well-being of children has been widely discussed, yet research-exploring strategies aimed at improving this in school-based settings is still an emerging field of research. This mixed methods study investigated the impact of a singing intervention on the subjective well-being of a class of 27 children aged 8–9. Over the course of 2 weeks, the class took part in 20 minutes of daily group singing with a focus on learning popular music that they chose. The sessions were delivered by a generalist primary teacher who had previously worked as a music specialist. In measuring children's subjective well-being with emphasis on life satisfaction, the ‘Student's Life Satisfaction Scale’ was administered to the children pre- and post-intervention. Of the 27 children, four (identified as disadvantaged) were interviewed as part of a focus group at the end of the intervention and questions centred around the children's opinions and enjoyment of the intervention. Results indicated that there was a much lower proportion of children with low subjective well-being scores after the intervention than before the intervention (as measured by the SLSS questionnaire). Analysis of the Likert scale data showed a ‘medium’ (d = 0.5) effect size. Thematic analysis of the focus group revealed that singing had a broadly positive effect on the well-being of those children, which is consistent with findings found in similar trials involving adults. Links to the theoretical framework of ‘flow’ by Csikszentmihalhi (1975) are drawn, alongside the PERMA well-being framework model (Seligman, 2012) to help explain the effects of being engrossed in an enjoyable activity such as group singing and how this in turn can impact subjective well-being.
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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 | 0.033 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".