Inclusion of Movement Behaviour in Early Childhood Education Frameworks in the United Kingdom
Bibliographic record
Abstract
Background: Early childhood education (ECE) settings are influential in shaping young children’s health behaviours. While the importance of healthy movement behaviour (i.e., physical activity, sedentary behaviour, sleep) on early childhood development is well understood, the extent to which these behaviours are incorporated in ECE frameworks in the United Kingdom (UK) has not been explored. As such, the aim of this study was to review current frameworks guiding the ECE landscape in the UK with a primary focus on examining inclusion of movement behaviours within these frameworks. Methods: ECE frameworks from each of the four nations of the UK were collected and reviewed. An extraction table was used to systematically retrieve content on physical activity, sedentary behaviour, screen time, sleep, indoor/outdoor infrastructure, outdoor play, and disability inclusion. Results: Physical development was found to be a core outcome for each nation, with emphasis on motor skill acquisition and outdoor play. However, there was minimal focus on physical activity, sedentary behaviour or screen time, and sleep. Interestingly, several of the frameworks (Scotland, Wales, Northern Ireland) suggested use of digital technology in ECE practices. All frameworks referenced regulations for providing additional support for children with disabilities, though there was minimal guidance on how activities or play spaces may meet these requirements.Conclusion: Strengthening ECE frameworks to prioritise healthy movement behaviours, including consideration of the mediator behaviours for physical development (i.e., physical activity, sedentary time) is crucial to support young children’s development.
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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 | 0.018 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".