Exploring the Utilization of a Wellness Framework for Children and Young People with Intellectual and Developmental Disabilities within Adaptive Sports Programs
Bibliographic record
Abstract
Introduction: Children and young people with Intellectual and Developmental Disabilities (IDD) have unique abilities and challenges that affect their health and wellness. Adaptive sports programs offer important opportunities to support their wellness. Objective: To explore the integration of a Wellness Framework into adaptive sports programs tailored for children and young people with IDD. Methods: 1) A rapid scoping review using Wellness Framework principles to provide a summary of sports coaching strategies to support the wellness of children and young people with IDD in a sports setting. 2) An explanatory sequential mixed-method study using non-participant observation to describe if and how the Wellness Framework principles were integrated into real-world adaptive sports programs run by Special Olympics. Results: The Wellness Framework was both supported in the literature and real-world sports programs and has been demonstrated to be a useful tool to help children and young people with IDD live their most meaningful lives. The summarized list of strategies to support the wellness of children and young people with IDD may be helpful for supporting their overall wellness if implemented by sports staff. Discussion: Sports coaches and health promotion staff have opportunities to purposefully integrate the strategies identified through both the scoping review and observational study to support the wellness of children and young people with IDD.
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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 | 0.021 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".