Can a teledance program be inclusive for children with and without motor disorders? A feasibility study
Bibliographic record
Abstract
The COVID-19 pandemic has significantly increased the use of remote physical activities and videoconference platforms. These changes could affect inclusion for children with motor disorders. This study aimed to evaluate the feasibility of an inclusive teledance program with pedagogical objectives for children with and without motor disorders. Twelve children aged 9 to 15 years (six with motor disorders and six without) participated in the program focused on learning basic dance knowledge and creating a choreography (ten classes; one hour twice/week over five weeks). Program’s feasibility was evaluated using acceptability (satisfaction and engagement questionnaires), adaptation and implementation (teachers’ notes and focus groups) criteria. Statistical analyses were conducted for quantitative data, while content, inductive and cross-case analyses were performed for qualitative data. Children reported high satisfaction and engagement levels. Adaptations planned by the teachers to ensure the program was inclusive and tailored to the constraints of a remote context are presented and discussed. Regarding the implementation criteria, high participation and retention rates were observed. Negative and positive factors affecting program implementation were examined under major themes (teledance, educational dance and inclusive dance). Overall, the results confirmed the feasibility of implementing an inclusive teledance program with both children with and without motor disorders.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".