Physical Activity Frequency of Special Olympic Athletes Ages 8-18 Across Economic Status
Bibliographic record
Abstract
The purpose of the study was to examine self-reported physical activity frequency of an international sample of-children and youth aged 8-17 who participate in Special Olympics across economic status. A secondary aim was to-determine if there was a difference between males and females in physical activity frequency across economic status. Data from 12,243 children and youth were available from the Special Olympics International Healthy Athletes Database after data cleaning (7819 male and 4424 female). Prevalence rates were calculated with confidence intervals for physical activity occurring less than three days per week, or three or more days per week across economic status of country (low; lower middle; upper middle and high income status). A series of Chi-square tests were used to determine the differences in physical activity frequency across economic status and gender. Overall, 65.4% of-Special Olympics participants from low-income, 40.8% from lower-middle-income, 50.8% from upper-middle-income, and 61.6% from high-income economies reported 3 or more days of physical activity per week. Additionally, male Special Olympic athletes tended to be more physically active than their female counterparts. Further research is needed to understand reasons for these differences and determine how to increase overall physical activity.
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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 | 0.001 | 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) | 0.002 | 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".