Physical play - How do we inspire and motivate young children to be physically active through play? An international analysis of twelve countries’ national early years curriculum policies and practices for physical activity and physical play
Bibliographic record
Abstract
Lifelong movement and physical activity (PA) patterns develop during early childhood.Therefore, educators (teachers and practitioners) in early childhood education and care (ECEC) should provide opportunities to support children's play, PA, and movement development.The World Health Organization (2019) offers new recommendations for PA, for children under five years.The guidelines do not specify the ways ECEC staff can support PA through play.Therefore, this paper investigates, how physical play (PP) is enacted globally.An international policy and practice analysis of twelve countries, (Australia [Victoria], Belgium [Flanders], Canada [Alberta], China, Finland, Ireland, Italy, Portugal, Spain, Sweden, UK [England] and USA) was completed by analyzing the ECEC curricula and their implementation in different cultural contexts.A content analysis was undertaken by AIESEP Early Years SIG experts revealing that PP was not clearly defined.When defined, it was described as PA, and important for children's holistic development.The majority of curricula did not state the length/time for PP.Three main strategies for implementing PP were found: a) pedagogical framework; b) active learning methods; and c) motor development.This international analysis highlights the global need for better ECEC staff support in acknowledging and implementing PP to aid children's overall development, PA and wellbeing.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".