Obesity prevention in early care and education: a comparison of licensing regulations across Canadian provinces and territories
Bibliographic record
Abstract
BACKGROUND: Early care and education (ECE) settings represent an important point of intervention for childhood obesity prevention efforts. The objective of this paper was to compare ECE licensing regulations for each Canadian province/territory to evidence-based, obesity prevention standards. METHODS: Two authors reviewed existing ECE regulations for each province/territory and examined whether the regulatory text supported standards for nutrition (n = 11), physical activity (n = 5) and screen time (n = 4). Provinces/territories were evaluated on the strength of regulatory language for each standard (i.e. fully, partially, or not addressed) and a total comprehensiveness score (maximum score of 20). ECE centres and homes were examined separately. RESULTS: The majority of provinces/territories required providers to follow Canada's Food Guide, but few had regulations for specific foods or beverages. Most provinces/territories included standards related to written menus and drinking water, but the strength of these standards was weak. Many provinces/territories required physical activity and outdoor opportunities to be provided daily, but few included a time requirement. Only two provinces included any screen time standards. Total comprehensiveness scores averaged 5.7 for centres and 5.4 for homes. CONCLUSIONS: Canadian provinces/territories have insufficient obesity prevention regulations in ECE settings, highlighting a potential point of intervention to prevent obesity.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".