Health education through football (soccer): the ‘11 for health’ programme as a success story on implementation: learn, play and have fun!
Bibliographic record
Abstract
Physical inactivity is one of the biggest public health threats of this century and greatly increases the risk of non-communicable diseases. This is especially true for our children and youth, where fallout from current pandemic lockdowns has disrupted access to physical activity and sport. Virtual classrooms translated into forfeited recess and free play while kids were thrown in front of computer screens and confined to a sedentary lifestyle. In large urban centres with many living in high rises, lockdowns on playgrounds and parks removed options for running or playing. Prior to the pandemic, physical inactivity was already a critical issue—a WHO survey of 1.6 million participants in 146 countries revealed that 81% of children and adolescents aged 11–17 were not active enough for optimal health.1 In response, WHO created the global action plan on physical activity, a call to action for policy change and effective implementation, where school-based policy initiatives are an essential component to create a more active society.2 In a recent …
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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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.021 | 0.029 |
| Insufficient payload (model declined to judge) | 0.016 | 0.011 |
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".