Impact of Changes in Playing Time on Playing-Related Musculoskeletal Pain in String Music Students
Bibliographic record
Abstract
BACKGROUND: During their training, musicians must develop good work habits that they will carry on throughout their professional career in order to avoid potential chronic health problems, such as musculoskeletal pain. The effect of sudden changes in instrument playing-time on the development of playing-related musculoskeletal pain (PRMP) has not been thoroughly investigated in music students playing bowed string instruments (BSI), even though they are regularly exposed to such changes to perfect their playing skills. OBJECTIVE: To explore the association between sudden changes in instrument playing-time and changes in PRMP in BSI players. METHODS: A prospective cohort study was completed with BSI students attending a summer music camp offering high-level training. Participants completed a self-administered 23-item questionnaire designed for the study upon arrival at camp (T1) and then 7 days later (T2). RESULTS: Ninety-three BSI students (16±4 yrs old) completed the questionnaires, for a 23% response rate. Their playing-time increased by 23±14 hrs between T1 and T2. Complaints in pain frequency (e.g., from never to most of the time) and intensity (19±24 mm on VAS) significantly increased between T1 and T2 and were correlated with an increase in playing-time. CONCLUSION: A sudden increase in playing-time, such as that experienced by elite BSI students attending an intensive music camp, was related to an increase in PRMP. However, in this study, changes in pain characteristics were only partly explained by the change in playing-time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".