Policies to Create Healthier Food Environments in Canada: Experts’ Evaluation and Prioritized Actions Using the Healthy Food Environment Policy Index (Food-EPI)
Bibliographic record
Abstract
Food environment policies play a critical role in shaping food choices, diets, and health outcomes. This study endeavored to characterize and evaluate the current food environment policies in Canada using the Healthy Food Environment Policy Index (Food-EPI) to compare policies in place or under development in Canada as of 1 January 2017 to the most promising practices internationally. Evidence of policy implementation from the federal, provincial, and territorial governments was collated and verified by government stakeholders for 47 good practice indicators across 13 policy and infrastructure support domains. Canadian policies were rated by 71 experts from across Canada, and an aggregate score of national and subnational policies was created. Potential policy actions were identified and prioritized. Canadian governments scored 'high' compared to best practices for 3 indicators, 'moderate' for 14 indicators, 'low' for 25 indicators, and 'very little or none' for 4 indicators. Six policy and eight infrastructure support actions were prioritized as the most important and achievable. The Food-EPI identified some progress and considerable gaps in policy implementation in Canada, and highlights a particular need for greater attention to prioritized policies that can help to shift to a health-promoting food environment.
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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 | 0.042 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".