Framework for the design and delivery of organized physical activity sessions for children and adolescents: rationale and description of the ‘SAAFE’ teaching principles
Bibliographic record
Abstract
The economic burden of inactivity is substantial, with conservative estimates suggesting the global cost to health care systems is more than US$50 billion. School-based programs, including physical education and school sport, have been recommended as important components of a multi-sector, multi-system approach to address physical inactivity. Additionally, community sporting clubs and after-school programs (ASPs) offer further opportunities for young people to be physically active outside of school. Despite demonstrating promise, current evidence suggests school-based physical activity programs, community sporting clubs and ASPs are not achieving their full potential. For example, physical activity levels in physical education (PE) and ASP sessions are typically much lower than recommended. For these sessions to have the strongest effects on young people's physical activity levels and their on-going physical literacy, they need to improve in quality and should be highly active and engaging. This paper presents the Supportive, Active, Autonomous, Fair, Enjoyable (SAAFE) principles, which represent an evidence-based framework designed to guide the planning, delivery and evaluation of organized physical activity sessions in school, community sport and ASPs. In this paper we provide a narrative and integrative review of the conceptual and empirical bases that underpin this framework and highlight implications for knowledge translation and application.
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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 | 0.038 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".