Physical health status of music students in a post-secondary institution: A cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Musicians' health is an essential field of healthcare that is specifically tailored to the needs of musicians, which encompasses multiple facets of health. OBJECTIVE: The research seeks to determine the prevalence of physical injuries in music students and musicians, and to identify possible causes. METHODS: A previously unvalidated 42-item survey was distributed to music students, non-music students, and professional musicians. The questions addressed demographics, physical health, mental health, medication use, and interest in musicians' health. The study was conducted from Fall semester 2017 to Winter semester 2019 at McGill University, with analysis completed in August 2019. RESULTS: A total of 585 complete responses were obtained. Music students (35%) had higher prevalence of physical injuries than non-music students (18%), and professional musicians had the highest prevalence (56%). Multiple factors dictate the prevalence of physical injuries among musicians, including gender, age, program of enrollment, and instrument of choice. Of note, daily duration of practice was not one of these factors. CONCLUSIONS: Several factors were identified through this cross-sectional analysis to be associated with musicians' physical injuries. These findings can serve as a foundation through which physicians and post-secondary institutions may implement changes to better enhance the physical health of musicians. It also cast doubts on previous assumptions associated with physical injury of musicians.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".