Global Matrix 4.0 on Physical Activity for Children and Adolescents: Project Evolution, Process Evaluation, and Future Recommendations
Bibliographic record
Abstract
BACKGROUND: The Global Matrix initiative provides unique insights into child and adolescent physical activity (PA) worldwide, yet requires substantial human efforts and financial support. PURPOSE: This study aimed to evaluate the process and outcomes of the latest edition of the initiative, the Global Matrix 4.0, reflect on its evolution from earlier editions, and provide recommendations for future Global Matrices. METHODS: The evaluation followed a predetermined plan, which included collecting and analyzing qualitative and quantitative data from 3 online surveys to participants and online user activity metrics from MailChimp reports and Google Analytics. RESULTS: Global Matrix 4.0 participants (57 teams, 682 experts) assessed the PA status in their country/jurisdiction on at least 10 PA indicators and submitted 570 grades for global comparisons. Surveys were completed by 97% to 100% of targeted respondents and demonstrated predominantly high satisfaction rates (>80%) with participation, outputs, and project management. Lack of funding and inadequate national PA data availability were the commonly reported concerns. Suggestions for improvement included amending indicators' benchmarks and expanding the scope of the initiative to early years, underrepresented populations, and additional indicators. CONCLUSIONS: This evaluation process revealed the positive experience of Global Matrix 4.0 participants and the successful delivery of expected outcomes. Reviewing the core set of indicators and benchmarks, expanding the initiative's scope, and fundraising efforts are recommended to further optimize the use of resources and maximize impact.
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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 | 0.318 | 0.163 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".