The Global Matrix of Physical Activity in Children and Adolescents in Latin America: trends, successes and challenges in practice and surveillance
Bibliographic record
Abstract
Objective: To synthesize the grades of physical activity (PA) indicators for children and adolescents (5-17 years) in Latin American countries; explore the social determinants of health (SDoH) for PA indicators; and identify strengths, weaknesses, opportunities, and threats to improve PA levels. Method: Participating Latin American countries graded a set of common PA indicators following the harmonized methodology established by the Global Matrix initiative. Cross-sectional (2014, 2016, 2018, 2022) and time trend (2018-2022) data were synthesized within and between countries for each PA indicator. PA data were also synthesized according to their SDoH. Report card team leaders completed a questionnaire to identify strengths, weaknesses, opportunities, and threats (SWOT) to improve PA grades. Results: = 193), 35.2% received a "D" (20%-39% success rate), the most frequent grade. Incomplete information was reported in 27.5% of the indicators. A 9.3% improvement was observed in the regional average score of all PA indicators analyzed over time. While source-of-influence indicators improved by 28.1%, behavioral indicators declined by 6.2%. The need for further analyses disaggregated by SDoH, such as sex, was identified. Conclusion: Latin American countries reported poor grades on PA indicators for children and adolescents. Contrasted progress was observed between the behavioral and source of influence indicator groups. Improved surveillance systems and greater country-level investment in PA data collection are urgently needed to enhance comparability and guide regional action.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".