Physical Activity Report Card Indicators and the United Nations Sustainable Development Goals: Insights From Global Matrix 4.0
Bibliographic record
Abstract
BACKGROUND: The World Health Organization recognizes that physical activity (PA) during childhood is crucial for healthy development, aligning well with the achievement of several United Nations (UN) Sustainable Development Goals (SDGs). This study aimed to explore the associations between 10 key indicators of PA for children and adolescents assessed in the Global Matrix 4.0 project, and the UN SDGs. METHODS: Data from 57 countries/jurisdictions of the Global Matrix 4.0 project were used. The UN SDG indicators were sourced from the SDG Transformation Center, which publishes each country's performance on each of the 17 SDGs. Given the robust evidence supporting plausible links between PA and SDGs 3 (good health and well-being), 9 (industry, innovation, and infrastructure), 11 (sustainable cities and communities), 13 (climate action), and 16 (peace, justice, and strong institutions), these SDGs were investigated. RESULTS: Countries/jurisdictions with good and moderate performance in achieving SDG 3, SDG 9, SDG 11, and SDG 16 had higher grades than countries/jurisdictions with fair performance in achieving these SDGs for the following indicators: Organized Sports and PA, Community and Environment, and Government Investments and Strategies. However, countries/jurisdictions with good performance in achieving SDG 13 had lower grades than countries/jurisdictions with fair performance in achieving SDG 13 for the following indicators: Organized Sports and PA, Community and Environment, and Government Investments and Strategies. CONCLUSIONS: Organized Sports and PA, Community and Environment, and Government Investments and Strategies were the indicators that demonstrated differences between countries/jurisdictions with good and poor performance in achieving the SDGs.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.015 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".