Worldwide variation in the performance of children and adolescents: An analysis of 109 studies of the 20-m shuttle run test in 37 countries
Bibliographic record
Abstract
This study is a meta-analysis of 109 reports of the performance of children and adolescents on the 20-m shuttle run test (20-mSRT). The studies were performed in 37 countries and included data on 418,026 children, tested between 1981 and 2003. Results were expressed as running speed (km x h(-1)) at the final completed stage of the 20-mSRT. Raw data were combined with pseudodata using Monte Carlo simulation. The 20-mSRT performances were expressed as z-scores relative to all children of the same age and sex from all countries. An overall "performance index" was derived for each country as the average of the age- and sex-specific z-scores for all children from that country. Factorial analysis of variance was used to compare scores among countries and regions, and between boys and girls of the same age. There was wide and significant (P < 0.0001) global variability in the performance of children. The best performing children were from the Northern European countries Estonia, Iceland, Lithuania, and Finland (0.6 - 0.9 standard deviations above the global average). The worst performing children were from Singapore, Brazil, USA, Italy, Portugal, and Greece (0.4 - 0.9 standard deviations below the global average). There is evidence that performance was negatively related to being overweight, as well as to a country's average temperature.
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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 | 0.024 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.018 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".