Introducing 24-Hour Movement Guidelines for the Early Years: A New Paradigm Gaining Momentum
Bibliographic record
Abstract
BACKGROUND: Emerging research shows that the composition of movement behaviors throughout the day (physical activities, sedentary behaviors, sleep) is related to indicators of health, suggesting previous research that isolated single movement behaviors maybe incomplete, misleading, and/or unnecessarily constrained. METHODS: This brief report summarizes evidence to support a 24-hour movement behavior paradigm and efforts to date by a variety of jurisdictions to consult, develop, release, promote, and study 24-hour movement guidelines. It also introduces and summarizes the accompanying series of articles related specifically to 24-hour movement guidelines for the early years. RESULTS: Using robust and transparent processes, Canada, Australia, New Zealand, South Africa, and the World Health Organization have developed and released 24-hour movement guidelines for the early years: an integration of physical activity, sedentary behavior, and sleep. Other countries are exploring a similar approach and related research is expanding rapidly. Articles related to guideline development in South Africa, the United Kingdom, Australia, and by the World Health Organization are a part of this special series. CONCLUSIONS: A new paradigm employing 24-hour movement guidelines for the early years that combines recommendations for movement behaviors across the whole day is gaining momentum across the globe.
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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 | 0.248 | 0.214 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.006 | 0.027 |
| Scholarly communication | 0.019 | 0.030 |
| Open science | 0.009 | 0.014 |
| Research integrity | 0.017 | 0.038 |
| Insufficient payload (model declined to judge) | 0.003 | 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".