The effects of musical practice on the well-being, mental health and social support of student, amateur, and professional musicians in Canada during the COVID-19 pandemic
Bibliographic record
Abstract
This pan-Canadian study investigates the effects of musical practice on the well-being, mental health, and social support of Canadian musicians during the COVID-19 pandemic. Using a survey questionnaire, data was collected from 1,618 participants aged 14 and above during the first wave of the pandemic up to the first half of 2022. The survey included standardized questionnaires to self-assess well-being (WHO-5), mental health (MHC-SF), and social support (SPS-10 measures social support). Results show that increased musical practice frequency correlates with improved well-being and mental health, particularly among amateurs. Professional musicians and those at a post-secondary level exhibit lower well-being scores, likely due to pandemic-related challenges. Factors such as age, gender, sports engagement, and participation in social clubs or volunteer work significantly influenced outcomes. While sports engagement was associated with higher scores on well-being, mental health and social support, no significant differences were found among participants engaged in artistic hobbies. As for involvement in social clubs or volunteer work, benefits were reported on two of the three outcomes. Overall, the findings suggest that regular amateur musical practice, especially in group settings, alongside engagement in sports and social activities, may have promoted well-being, mental health, and social support among musicians during the challenging period of the COVID-19 pandemic.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".