Wheelchair Dance: Exploring a Novel Approach to Enhance Wheelchair Skills, Belongingness and Inclusion among Children with Mobility Limitations
Bibliographic record
Abstract
Playful approaches are recommended to enhance wheelchair skills training with young people. Inclusive dance allows participants to discover motor skills and improve social participation. Integrating wheelchair skills training into dance has not been evaluated. This study aimed to explore participants’ experiences in dance while integrating wheelchair skills, and the influence of dance on wheelchair skills and wheelchair use confidence in young people. A convergent mixed-methods design was used during a one-week dance camp. Data collection combined observations, two focus groups (with young dancers who used manual wheelchairs and with professional dancers without disabilities), and evaluation of wheelchair skills and confidence. Data analyses included deductive thematic analysis guided by the Quality Parasport Participation Framework, merged with pre–post comparisons in wheelchair skills and confidence. Three young female dancers were 11, 12 and 15 years of age and three professional female dancers were 22, 27 and 27 years of age. Emergent themes included skill mastery, belongingness, and supportive environments. There were improvements in wheelchair skills and confidence (16.7%, 19.4%, 16.7%; 0.8%, 11.4%, 4.5%, respectively). Participants described overall positive experiences with the dance camp and perceived enhanced skills and confidence. This study advances knowledge about innovative approaches to integrate wheelchair skills training for young people. Future larger-scale controlled studies are needed to determine efficacy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".