‘It shouldn’t be necessary, but it happens a lot’: Undergraduate contemporary dancers’ perceptions of pain, injury, and fatigue
Bibliographic record
Abstract
Contemporary dancers are at risk for musculoskeletal injury due to the choreographic, artistic, and physical demands of the form. Group norms such as persevering through and normalization of pain and injury have been demonstrated within dance contexts and may contribute to the high prevalence of reported injury among dancers. The purpose of this study was to explore contemporary dancers’ perceptions of pain, injury, and fatigue. Ten undergraduate contemporary dance students, self-identifying as women (18-23 years), participated in semi-structured interviews. Interviews were analyzed using reflexive thematic analysis. Four themes were generated: (1) It’s more abnormal for people to sit out than to dance through their injuries; (2) Pain, injury, and fatigue aren’t just physical; (3) Safe dancing environments are important; (4) Implementing dancer health knowledge into practice is complicated. Participants described training and performing through pain and injury, and not always feeling comfortable expressing these experiences to instructors. Pain, injury, and fatigue impacted participants physically and psychologically. Safe dance practice education was viewed as important but challenges implementing knowledge into practice were noted. Continued efforts to integrate safe dance practices into university dance curriculum are needed to help minimize the high prevalence of pain, injury, and fatigue among university contemporary 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".