Bibliographic record
Abstract
PURPOSE: Injuries described as 'playing-related' in musicians have a prevalence rate of up to 87%. Nevertheless, healthcare consultation rates among this population remain low. Using the theoretical framework of the World Health Organization's International Classification of Functioning, Disability and Health (ICF), the author examined the musicians' health literature to determine potential causes for the shortcomings in the provision of evidence-based treatment for injured musicians. METHOD: A review of the literature on musicians' health was performed from 1998 to December 2005. In total, 131 papers were reviewed. RESULTS: Much of the literature on musicians' health is related to the domain of Body Structure and Function within the ICF framework. However, even in these relatively well-defined areas, clinicians appear to agree that the 'best treatment' depends enormously on the interplay of biopsychosocial factors and on the musician's context. Underrepresented areas in the literature include Environmental Factors, Personal Factors, and the domain of Participation. CONCLUSIONS: This review demonstrates a lack of research in some health and health-related domains in the musicians' health literature. The author suggests that these underrepresented areas within the ICF framework may explain shortcomings in the availability of effective, evidence-based treatment for musicians. The use of a framework to shape future research in this field is advocated.
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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 | 0.044 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.025 | 0.021 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.007 | 0.005 |
| 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".