The Future of Para Report Cards on Physical Activity of Children and Adolescents With Disabilities—A Global Call for Engagement, Data, and Advocacy
Bibliographic record
Abstract
The Active Healthy Kids Global Alliance (AHKGA) is an international not-forprofit organization of researchers, health professionals, and stakeholders working together to advance the physical activity of children and adolescents around the world.Established in 2014, the AHKGA has a vision of a world of active, healthy kids.The AHKGA is committed to powering the global movement to get kids moving through thought leadership, knowledge translation/exchange and mobilization, capacity building, and advocacy, facilitated by sustainable partnerships and cross-sectoral collaborations that enable best practice exchanges, networking, and cross-fertilization.The dominant effort of the AHKGA has been the Global Matrix initiative (Aubert et al., 2022;Tremblay et al., 2022).The Global Matrix initiative involves the collaboration of multiple countries/jurisdictions working together to produce Report Cards on the physical activity of children and adolescents.The initiative follows a harmonized process that allows for comparability, crossfertilization of ideas to improve the grades, global networking, and the compilation of international data across a series of indicators related to the physical activity of children and adolescents.To date, >70 countries/jurisdictions have produced Report Cards based on the harmonized process, and 68 countries/jurisdictions have
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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 | 0.033 | 0.122 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.025 | 0.030 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
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".