Gaps in Nutrition Policy Implementation in Childcare Centres in The Edmonton Metropolitan Region: A Cross-Sectional Survey
Bibliographic record
Abstract
Purpose: To describe (i) nutrition policies in childcare centres, (ii) the resources and processes used to enable policy implementation, and (iii) the association between policy implementation and childcare centres’ or administrators’ characteristics. Methods: Between October 2018 and June 2019 a web-based survey that addressed nutrition policy, policy implementation, and sociodemographic characteristics was sent to eligible childcare programs (centre-based and provided meals) in the Edmonton (Alberta) metropolitan region. The survey was pretested and pilot tested. Statistical tests examined the relationship between policy implementation with centres’ and administrators’ characteristics. Results: Of 312 childcare centres that received the survey invitation, 43 completed it. The majority of centres had a nutrition policy in place (94%). On average, centres had about 9 of the 17 implementation resources and processes assessed. Most often administrators reported actively encouraging the implementation of the nutrition policy (n = 35; 87%) and least often writing evaluation reports of the implementation of the nutrition policy (n = 9; 22%). Administrator’s education level was associated with implementation total score (p = 0.009; Kruskal-Wallis). Conclusion: Most childcare centres had a nutrition policy in place, but many lacked resources and processes to enable policy implementation. Additional support is required to improve nutrition policy development and implementation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".