Body Mass Index of Adult Special <scp>O</scp>lympians by Country Economic Status
Bibliographic record
Abstract
Abstract Many low‐ and middle‐income countries have experienced an epidemic of obesity in the last few decades. However, no studies have examined the relationship between country economic status and weight status among adults with ID. This study compared the prevalence of underweight, normal weight, overweight, and obesity among adult Special Olympics participants by country economic status. A total of 19,295 (men, n = 12,037) measured height and weight records were available from the Special Olympics International (SOI) Health Promotion database. The 159 countries in the database were recoded according to the World Bank's classification of country economic status as: low‐income, lower middle‐income, upper middle‐income, and high‐income. Body mass index (BMI; kg/m2) prevalence rates were calculated for underweight, normal weight, overweight, and obesity for men and women by economic status. Odds ratios, adjusted for age and sex, were used to examine differences in BMI by country economic status. Overall, 31.9% of SOI participants from low‐income economies, 48.6% from lower middle‐income, 43.6% from upper middle‐income, and 66.0% from high‐income economies had BMI indices outside of the normal range. For the low‐income countries, the proportion of underweight and overweight/obesity was similar (17.2% and 14.7%, respectively). For the other three levels of economy, participants with BMI levels outside the normal range were largely overweight/obese, rather than underweight. Women, older participants, and those from higher‐income countries were much more likely to be overweight/obese. Considerably, more research on the key behaviors associated with BMI status and the extent to which environments (economic, social, and physical) are obesogenic is needed to explain these differences and to begin to design interventions that can be both targeted for persons with ID and coherently implemented across sectors and settings.
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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) | 0.004 | 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".