Injury Prevention Strategies in Dance: A Systematic Review
Bibliographic record
Abstract
Background: Dance is a popular activity worldwide that comes with inherently high injury rates. Despite the call for implementation and evaluation of injury prevention strategies, the utility and efficacy of injury prevention practices in dance are unknown. Objective: The primary objective of this systematic review was to describe dance-specific injury prevention strategies in any dance population. The secondary objective was to evaluate the effectiveness of these strategies in the reduction of injury prevalence or incidence rates. Methods: Six electronic databases were searched to March 2024 (Medline, EMBASE, SportDiscus, CINAHL, SCOPUS, Cochrane). Selected studies met a priori inclusion criteria that required original data describing an injury prevention strategy for any genre of dance. Studies were excluded if; no injury outcomes were reported, no injury prevention strategy was utilized, or if dance was used as a therapeutic intervention/fitness. Two reviewers independently assessed abstracts and full texts for inclusion. Study quality was evaluated using the Joanna Briggs Institute Level of Evidence tool. Results: From 2209 potentially relevant records, 8 studies met criteria for inclusion. Injury prevention strategies were diverse across conditioning and exercise programs, neuromuscular training warm-up, psychological coping skills training, and educational programs, with a range of duration and adherence measures. Due to inconsistent injury surveillance methodology, efficacy of these programs could not be determined. Quality of included studies was moderate to high, with moderate to low risk of bias. Conclusions: Efficacy and adherence to dance-specific injury prevention strategies are understudied in the dance science literature. Conditioning and exercise programs are currently the most common injury prevention strategies, with initial evidence suggesting they may be efficacious in reducing injury rates. Further research using valid injury surveillance methodologies and standardized injury definitions to examine the efficacy of these strategies in reducing dance-related injury is needed. Level of evidence: I.
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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 | 0.012 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".