Community‐Informed Recommendations to Developing Inclusive Dance Opportunities: Engaging Community, Dance, and Rehabilitation Experts Using a Hybrid‐Delphi Method
Bibliographic record
Abstract
BACKGROUND: Amongst the growing number of examples of inclusive dance programming, community-informed recommendations for inclusive dance are scarce. Our purpose was to develop recommendations for inclusive dance with dancers with the lived experience of autism, intellectual, or developmental disability, and the professionals who work with them. METHOD: A Hybrid-Delphi method was used to generate and rank recommendations across three expert groups. The constraints model of motor development and the social model of disability framed three questions around: (i) physical environment and culture, (ii) instruction and strategies, and (iii) dance assistants. RESULTS: The experts (Community; dancers, support persons/carers (n = 5), Rehabilitation (n = 6) and Dance (n = 7) professionals) agreed to prioritise the community perspective, highlighting the need for ongoing education around inclusive instruction, communication, and sensory considerations. CONCLUSIONS: The centring of community perspectives facilitated the development of a comprehensive list of actionable recommendations to guide inclusive dance instruction in a variety of dance spaces.
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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 | 0.103 | 0.087 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".