One-Year Injury History and Safe Dance Practices Among Female Highland Dancers in Canada
Bibliographic record
Abstract
AIMS: Over 8,650 Highland dancers registered to compete in Royal Scottish Official Board of Highland Dancing events worldwide in 2019. While the burden of dance-related injuries is high among dancers, there are few studies examining Highland dance. The purpose of this study was to describe the prevalence of self-reported 1-year injury history and safe dance practices among female Highland dancers. METHODS: Sixty-five female Canadian Highland dancers (median age 18; range 14-47) completed an anonymous online survey at the beginning of the 2019 championship season. Demographics (i.e., age, body mass index), exposure (e.g., months/year dancing), safe dance practices (e.g., environmental, physical, psychological), and 1-year injury history (i.e., yes/no) were self-reported. Three definitions of dance-related injury were used: 1) time-loss (missed ≥1 class, practice, performance, and/or competition); 2) medical attention (requiring professional medical care); and 3) any physical complaint that affected full participation. RESULTS: Most participants were training at the elite standard/premier level (86%, 95%CI 75-93) and for ≥8 months/year (83%, 95%CI 75-93). The proportion of dancers reporting at least one physical complaint in the previous 1 year was 71% (95%CI 58-81). Sixty percent (95%CI 47-71) of dancers reported ≥1 medical attention and/or time-loss injury. All participants reported warming up regularly, with 59% (95%CI 46-70) participating in regular cool-downs. CONCLUSION: The prevalence of 1-year injury history among female Highland dancers is high. Education on the benefits of safe dance practice for Highland dancers may be useful. Prospective cohort studies are needed to understand the dynamic nature of dance injuries across a full competitive season.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".