Musculoskeletal Injury in Professional Dancers
Bibliographic record
Abstract
OBJECTIVE: The purpose of the study was to determine the prevalence and factors associated with injury in professional ballet and modern dancers, and assess if dancers are reporting their injuries and explore reasons for not reporting injuries. DESIGN: Cross-sectional study. SETTING: Participants were recruited from nine professional ballet and modern dance companies in Canada, Denmark, Israel, and Sweden. PARTICIPANTS: Professional ballet and modern dancers. INDEPENDENT VARIABLES: Sociodemographic variables included age, sex, height, weight, and before-tax yearly or monthly income. Dance specific characteristics included number of years in present dance company, number of years dancing professionally, number of years dancing total, and rank in the company. MAIN OUTCOME MEASURES: Self-reported injury and Self-Estimated Functional Inability because of Pain. RESULTS: A total of 260 dancers participated in the study with an overall response rate of 81%. The point prevalence of self-reported injury in professional ballet and modern dancers was 54.8% (95% CI, 47.7-62.1) and 46.3% (95% CI, 35.5-57.1), respectively. Number of years dancing professionally (OR = 4.4, 95% CI, 1.6-12.3) and rank (OR = 2.4, 95% CI, 1.2-4.8) were associated with injury in ballet dancers. More than 15% of all injured dancers had not reported their injury and their reasons for not reporting injury varied. CONCLUSIONS: The prevalence of injury is high in professional dancers with a significant percentage not reporting their injuries for a variety of reasons. Number of years dancing and rank are associated with injury in professional ballet dancers.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".