Using Behavior Analysis and Therapy to Teach Dance to Neurodiverse Children in Day Treatment Education Program
Bibliographic record
Abstract
Neurodiverse children in day treatment programs often experience behavioral challenges that limit their opportunities to engage in recreational physical activities. These activities are important for physical health and aid in developing motor and socio-emotional skills. The present study used an explanatory sequential mixed methods design to evaluate the effectiveness of Dance With A B-E-A-T! (Behavior-Analysis-and-Therapy), a community-based program combining dance with applied behavior analysis (e.g., modeling, reinforcement), to teach three dance sequences to five neurodiverse participants (7-9 years) in a day treatment program. Within five sessions, the mean percentage of steps completed correctly increased from a combined average of 31.5% (range = 18.9-52.0%) to 61.4% (range = 53.3-72.0%) for all three dance sequences, two of which were statistically significant (p < .05). The participants described their experience as “fun”, “good”, and “happy”, and program counselors reported high consumer satisfaction, suggesting Dance With A B-E-A-T! benefited both participants and staff.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".