Tracking Physical Activity and Nutrition Policies and Practices in Early Childhood Education and Care: Five Years Post-Implementation of a Provincial-Level Active Play Standard
Bibliographic record
Abstract
Background: Early childhood education and care (ECEC) settings are key for improving health behaviors, including physical activity (PA) and nutrition. In 2017, the province of British Columbia (BC) implemented a provincial-level Active Play policy supported by a capacity-building intervention. Significant improvements in all PA policies and practices and the majority of nutrition policies were observed post-implementation. The purpose of this study was to understand if PA and nutrition policies and practices were maintained at 5+ years post-provincial policy implementation. Methods: This study employed a repeated cross-sectional design to distribute surveys querying about PA and nutrition policies and practices to ECEC centers across BC at three time points: time 1, prior to implementation of the Active Play standard (2016–2017) and capacity-building intervention, time 2, 1–2 years post-implementation (2018–2019), and time 3, 5+ years post-implementation (2022–2023). Results: The majority of PA and all nutrition policies were maintained from time 2 ( n = 378) to time 3 ( n = 639). Prevalence of policies related to the provision of activities that address fundamental movement skills (odds ratio [OR] = 0.30) and total amount of active play (OR = 0.56) significantly decreased from time 2 to time 3. All reported PA practice prevalence levels decreased to time 1 levels. Conclusions: Center-level health behavior policies were largely maintained 5 years post-implementation, except some PA policies and practices returned to pre-implementation levels. Staff capacity and turnover as well as change in implementation support may explain these changes. Ongoing implementation support is needed to ensure maintenance of health promoting policies and practices in ECEC.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".