Association Between Physical Activity Indicators and Human Development Index at a National Level: Information From Global Matrix 4.0 Physical Activity Report Cards for Children and Adolescents
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to explore the associations between the 10 key indicators of the Global Matrix 4.0 project and human development index (HDI) at a national level according to sex, age, area of residence, and ability levels. METHODS: Information from the 57 countries/localities included in the Global Matrix 4.0 project was compiled and presented according to the HDI of each country/locality for each of the 10 key indicators. Grades were assigned based on the benchmarks of the Global Matrix 4.0 project ranged between "A+" (best performance) and "F" (worst performance). RESULTS: The population subgroups of females, children, rural residents, with/without disabilities from countries/localities with higher HDI performed better in the organized sport and physical activity indicator than their peers from countries/localities with lower HDI. Children and adolescents living in rural areas of countries/localities with higher HDI showed better performance for active play, and children and adolescents living in urban areas of countries/localities with lower HDI showed better performance for the active transportation. Countries/localities with higher HDI showed better grades for sources of influence than the countries/localities with lower HDI. CONCLUSIONS: Physical activity patterns in some population subgroups of children and adolescents differed according to the development level of countries/localities.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".