MusicCohort: Cross-sectional feasibility study of an assessment protocol for student musicians
Bibliographic record
Abstract
Abstract This study examined the feasibility of employing an assessment battery developed in Germany, investigating mental and physical health of university music students, in Canada. Using a cross sectional design, first-semester music and control students were recruited at two university campuses. Eligibility criteria were: 1) first-semester, full-time undergraduate music major (case) or in another university program (control), 2) over the age of 16. Exclusion criteria were: 1) diagnosis of neurological, orthopaedic or psychological condition, 2) diagnosis of infection or systemic disease, 3) regular consumption of medication for pain or mental health diagnosis, 4) varsity athlete, 5) for controls only, enrollment in music courses where a grade is assigned for music performance (e.g. studio lessons). Both groups completed questionnaires and physical testing, including range of motion, core strength and pressure pain threshold. Data for 19 music and 50 control students were analysed. Musician participants demonstrated tendencies towards poorer mental and physical health. This German protocol is feasible in a Canadian university setting. Canadian music students demonstrate similar mental and physical health outcomes to those in the literature and in the parent study. The results of this feasibility study should be confirmed in a larger study.
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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 | 0.124 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".