Evaluation of the process and outcomes of the Global Matrix 3.0 of physical activity grades for children and youth
Bibliographic record
Abstract
BACKGROUND/OBJECTIVE: Participation in the Active Healthy Kids Global Alliance (AHKGA) Global Matrix initiative represents a significant work and resource investment for Report Card teams. The objective of this paper was to evaluate the process and findings of the Global Matrix 3.0 and formulate recommendations for improvement. METHODS: of the AHKGA website, and emails sent to the AHKGA Board of Directors. RESULTS: Five online surveys were completed by 88%-100% of the targeted respondents. High satisfaction ratings were observed for most of the Global Matrix 3.0 methods, key steps, concepts, and the resources (e-blasts and website) provided by the AHKGA. A total of 496 open-ended comments were provided in the five surveys, including 199 comments reporting issue(s), and 38 reporting both positive feedback and issue(s). The participating Report Card teams successfully assigned a grade to each physical activity indicator, produced a Report Card document, and wrote a short Report Card article. CONCLUSION: This evaluation process allowed for the identification of needed improvements and the formulation of recommendations for future Global Matrix initiatives. This work highlighted the need for the development of physical activity behavior assessment tools that would be internationally adopted and culturally adaptable to varying contexts to improve the standardization of physical activity surveillance at the global scale.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.061 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".