Muscular strength of adult Special Olympians by country economic status
Bibliographic record
Abstract
There is a strong relationship between economic prosperity and health as well as between muscle strength and health (morbidity and mortality). However, very little research has concomitantly examined economic prosperity and muscular strength in the general population, and no studies have simultaneously examined these factors in a population of adults with intellectual disabilities. This study examined grip strength among adult Special Olympics participants by country economic status. A total of 12,132 (men = 65%) right and left hand grip strength records were available from the Special Olympics International (SOI) FUNFitness database. The 127 countries within the SOI dataset were grouped by economy according to The World Bank's gross national income per capita as: low-income countries (n = 11), lower middle-income countries (n = 27), upper middle-income countries (n = 38), and high-income countries (n = 51). There was a significant overall effect of country economic status for both males and females for right and left hand grip strength. Although the grip strength of both men and women did not differ between low-income and low-middle income countries, the general trend was to observe greater grip strength with increased economic prosperity among both men and women. However, to advance our knowledge of the importance of muscle strength for persons with intellectual disabilities, research linking grip strength to health outcomes, functional status, and successful participation activities of daily living is needed.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".