A Survey of Injuries Affecting Pre-Professional Ballet Dancers
Bibliographic record
Abstract
A cross-sectional design was employed retrospectively to evaluate injuries self-reported by 71 pre-professional ballet dancers over one season. Some of the descriptive findings of this survey were consistent with those of previous research and suggest particular demographic and injury trends in pre-professional ballet. These results include gender distribution, mean age and age range of participants, training hours, injury location, acute versus overuse injuries, as well as average number of physiotherapy treatments per dancer. Other results provide information that was heretofore unreported or inconsistent with previous investigations. These findings involved proportion of dancers injured, average number of injuries per dancer, overall injury incidence during an 8.5 month period, incidence rate by technique level, mean time loss per injury, proportion of recurrent injury, and activity practiced at time of injury. The results of univariate analyses revealed several significant findings, including a decrease in incidence rate of injury with increased months of experience in the pre-professional program, dancers having lower injury risk in rehearsal and performance than in class, and a reduced risk of injury for dancers at certain technique levels. However, only this latter finding remained significant in multivariate analysis. The results of this study underscore the importance of determining injury rates by gender, technique level, and activity setting in addition to overall injury rates. They also point to the necessity of looking at both overall and individual dancer-based injury risks.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".