Step-by-step: Establishing expert informed recommendations for inclusive and adaptive dance instruction using a hybrid-Delphi technique
Bibliographic record
Abstract
Dance is a form of expression that spans cultural and socioeconomic contexts. In Canada dance curricula exist in both dance and education contexts, yet expert informed guidelines for inclusive/adaptive dance environments are scarce. The purpose of the present study was to develop a list of recommendations for inclusive/adaptive dance opportunities through the lens of critical experts who have experience with, or identify as having, neurodevelopmental disability (NDD). A hybrid-Delphi technique was used: i) to collect and articulate expert perspectives on methods of adapting dance environments and instruction; and ii) to formulate their contributions into a set of prioritized recommendations to be shared with the dance community. Over five months three expert groups (rehabilitation professionals (n=6), dance educators (n=7), dancers and support persons/carers (n=5)) engaged in virtual focus groups to generate statements, followed by an e-survey process to rank and consolidate statements. The constraints model of motor development and the social model of disability were used to frame questions around: i) physical environment and culture, ii) instruction and strategies, and iii) assistants/carers; and informed the reduction and ranking of responses. Three key themes were identified: i) adapting the environment (physical, culture of, and instruction, ii) how to adapt instruction and strategies, and iii) ongoing communication and education. Expert discussions emphasized the importance of centering the perspectives of dancers with NDD and their support persons/carers (community group) in the final list of recommendations, as well as in ongoing education, dance spaces and culture, and communication of instructions.
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 | 0.120 | 0.134 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".