The International Impact of the Active Healthy Kids Global Alliance Physical Activity Report Cards for Children and Youth
Bibliographic record
Abstract
BACKGROUND: In response to growing concerns over high levels of physical inactivity among young people, the Active Healthy Kids Global Alliance developed a series of national Report Cards on physical activity for children and youth to advocate for the promotion of physical activity. This article provides updated evidence of the impact of the Report Cards on powering the movement to get children and youth moving globally. METHODS: This assessment was performed using quantitative and qualitative sources of information, including surveys, peer-reviewed publications, e-mails, gray literature, and other sources. RESULTS: Although it is still too early to observe a positive change in physical activity levels among children and youth, an impact on raising awareness and capacity building in the national and international scientific community, disseminating information to the general population and stakeholders, and on powering the movement to get kids moving has been observed. CONCLUSIONS: It is hoped that the Report Card activities will initiate a measurable shift in the physical activity levels of children and contribute to achieving the 4 strategic objectives of the World Health Organization Global Action Plan as follows: creating an active society, creating active environments, creating active lives, and creating active systems.
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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 | 0.056 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".