Prevalence and Relationships among Physical Activity Policy, Environment, and Practices in Licensed Childcare Centers from a Manager and Staff Perspective
Bibliographic record
Abstract
Physical activity (PA) is critical to early childhood health and development, and childcare is a key setting for establishing physically active play. In British Columbia (BC), a provincial standard for active play in childcare was enacted, identified here as the Active Play (AP) standard. Pragmatic constraints limit real-world data collection for evaluating policy impact. We explored whether information about policies, practices, and the environment varied when it was collected from managers or staff. Surveys were distributed to BC childcare centers before AP standard enactment to ascertain current PA and fundamental movement skill policies and practices. The full sample (n = 1037 from 625 facilities) and a subsample of paired managers and staff (n = 261 centers) were used to explore agreement across managers and staff in reported prevalence and relationships among indicators. The policy prevalence and relationships for active play and outdoor play variables were relatively similar for manager and staff data, although the matched data had modest agreement and less than optimal intraclass correlations. The prevalence of manager-reported PA policies ranged from 47% for screen-time limits to 77% for fundamental movement skill activities. The manager and staff data highlighted indoor and outdoor space as a primary factor in AP standard adherence. With reliance on sampling staff unfeasible, it appears that the manager data may adequately describe the policies and practices of childcare providers with some notable issues.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".