Examining the Preferences and Priorities of Dance Educators for Dance Science Information: A Pilot Study
Bibliographic record
Abstract
INTRODUCTION: The growing field of dance medicine and science provides dance educators the opportunity to incorporate evidence-based approaches into teaching practices. Incorporating knowledge produced by dance science research into evidence-based practice can improve learning and health outcomes for dance students. Guided by the Knowledge to Action (KTA) Framework, the purpose of this study was to examine the preferences and research priorities of dance educators for receiving, accessing, and implementing dance science knowledge. METHODS: Ninety-seven dance educators representing a range of styles, experience, and educational settings completed an online survey. Dance educators responded to questions about the dance science topics they felt were important to their teaching practices, their preferences for receiving dance science information, and areas of dance science that need more research. Results:Responses indicated that dance science was important to participants' teaching practices although there was variability in which dance science topics were seen as "Absolutely Essential." Participants reported a preference for receiving dance science information through in-person methods and observations. Variability was also shown in participant responses to statements about the accessibility, format, and applicability of dance science information to teaching practices. Dance educators indicated that the easiest dance science topics to find information about were anatomy, flexibility, biomechanics, and injury prevention; dance educators also identified that more research was needed in mental health and psychology. CONCLUSION: The findings of this survey provide key considerations for factors such as accessibility, specificity, and resources that are user-friendly to inform future knowledge translation efforts tailored to dance educators.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".