Towards a successful teledance program for youth with cerebral palsy: A mixed-method study with the instructor’s perspective
Bibliographic record
Abstract
PurposeDance is a leisure time physical activity (LTPA) known to improve motor, cognitive, and psychosocial functions in youth with cerebral palsy (CP). Online exercise or tele-programs are promising in overcoming the environmental barriers of accessibility to LTPA. To ensure successful implementation, it is necessary to identify limitations specific to dance in a pediatric population. The aim was to explore the perspectives of the main stakeholders, i.e., dance instructors and youth, to implement such a program.MethodsIn a mixed-method design, feasibility indicators were assessed by participation and retention rates, the Physical Activity Enjoyment Scale (PACES), and the Children's Effort Rating Table (CERT). Semi-structured interviews were conducted before and after the intervention with youth with CP [n = 15] and dance instructors [n = 3]. Interviews were analyzed with an inductive approach.ResultsParticipation and retention rates were 86.7% ± 10.7 and 100%, and the PACES and CERT average scores were 91% ± 11 and 3.7 ± 1.3, respectively. Four themes emerged from the interviews: 1) Technology; 2) Pedagogical Approach; 3) Participant's Environment; and 4) Social Relations.ConclusionThe teledance program is feasible and enjoyable, requiring minimal equipment and travel. However, there is a need to consider and provoke social interaction, to enhance the social and relational dimension of dance.
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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 | 0.009 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".