Body mass index of adults with intellectual disability participating in <scp>S</scp>pecial <scp>O</scp>lympics by world region
Bibliographic record
Abstract
BACKGROUND: People with intellectual disability (ID) experience poorer health and have more unmet health needs compared with people without ID, and they are often absent from population health surveillance. The aim of this study was to describe the body mass index (BMI) status of adult Special Olympics participants by world region and gender. Additionally, the general influence of age and gender on overweight/obesity of all participants was explored. METHOD: A total of 11 643 (7150 male and 4493 female) Special Olympics BMI records were available from the Special Olympics International Health Promotion database. BMI was compared by gender and world region. Logistic regression was used to examine whether age and gender were associated with the likelihood of being overweight/obese (BMI ≥ 25.0). RESULTS: Overall, 5.5% of the sample was underweight, 36.1% in the normal range, 24.7% overweight and 32.1% obese, and levels of overweight/obesity were very high in North America. Both age and gender were significant predictors of overweight/obesity (odds ratios 1.06 and 0.59, respectively). CONCLUSIONS: Our findings demonstrate that adult Special Olympics participants have high levels of overweight and obesity; particularly among women and those from North America. It is crucial that those who work with, care for, coach and live with adults with ID who participate in Special Olympics increase efforts to promote healthy weight status.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 0.001 |
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".