Participation in Special Olympics reduces the rate for developing diabetes in adults with intellectual and developmental disabilities
Bibliographic record
Abstract
AIM: Adults with intellectual and developmental disabilities (IDD) have a significantly higher prevalence of Type 2 diabetes than the general population. Evidence that lifestyle and/or behavioural interventions, such as participation in Special Olympics, decreases the risk of developing diabetes in adults with IDD could help minimize health disparities and promote overall health in this population. METHODS: This was a 20-year retrospective cohort study of adults with IDD (30-39 years) in the province of Ontario, Canada, that compared hazard rates of diabetes among Special Olympics participants (n = 4145) to non-participants (n = 31,009) using administrative health databases housed at ICES. Using cox proportional hazard models, crude and adjusted hazard ratios were calculated for the association between the primary independent variable (Special Olympics participation status) and the dependent variable (incident diabetes cases). RESULTS: After controlling for other variables, the hazard ratio comparing rates for developing diabetes between Special Olympics participants and non-participants was 0.85. This represents a 15% reduction in the hazard among Special Olympics participants when followed for up to 20 years. This result was statistically significant and represents a small effect size. CONCLUSIONS: Special Olympics could be considered a complex intervention that promotes physical activity engagement through sport participation, health screenings, and the promotion of healthy eating habits through educational initiatives. This study provides evidence that Special Olympics participation decreases the rate for developing diabetes.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".