Exploring the world of active play: A comprehensive review of global surveillance and monitoring of active play based on the global matrix data
Bibliographic record
Abstract
A valid assessment tool that measures active play is not yet available due to the sporadic and spontaneous nature of play, as well as the potential differences in how active play is understood and measured across different age groups, cultures, and contexts. The purpose of this review was to identify the scope and gaps in the measurement of active play based on data gathered from 68 countries that participated in the Global Matrix (GM) initiative, led by the Active Healthy Kids Global Alliance (AHKGA). GM is the global-level, biennial evaluation system of physical activity related behaviors among children and youth, including the Active Play indicator, and the sources of influence using letter grades (ranging between "A" and "F"). Based on the identified scope and gaps, this study offers recommendations for future research dedicated to the measurement/surveillance of active play. Out of the 68 countries involved in the previous GM (2014-22), 55% of the grades remained unassigned due to insufficient data on the Active Play indicator. The high number of unassigned grades, combined with the absence of valid measurement tool, highlight a need for a standardized measurement tool for improved global data generation of active play among children and youth. Our findings emphasize the need to address challenges in measuring active play. This review offers future considerations, research recommendations specific to the GM initiative, and two sets of age- and location-specific (indoor and outdoor settings) questionnaire items along with guidelines for its use. Together, these elements provide a roadmap for guiding future research and evaluation efforts on active play.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".