An 11‐country study to benchmark the implementation of recommended nutrition policies by national governments using the Healthy Food Environment Policy Index, 2015‐2018
Bibliographic record
Abstract
The Healthy Food Environment Policy Index (Food-EPI) aims to assess the extent of implementation of recommended food environment policies by governments compared with international best practices and prioritize actions to fill implementation gaps. The Food-EPI was applied in 11 countries across six regions (2015-2018). National public health nutrition panels (n = 11-101 experts) rated the extent of implementation of 47 policy and infrastructure support good practice indicators by their government(s) against best practices, using an evidence document verified by government officials. Experts identified and prioritized actions to address implementation gaps. The proportion of indicators at "very low if any," "low," "medium," and "high" implementation, overall Food-EPI scores, and priority action areas were compared across countries. Inter-rater reliability was good (GwetAC2 = 0.6-0.8). Chile had the highest proportion of policies (13%) rated at "high" implementation, while Guatemala had the highest proportion of policies (83%) rated at "very low if any" implementation. The overall Food-EPI score was "medium" for Australia, England, Chile, and Singapore, while "very low if any" for Guatemala. Policy areas most frequently prioritized included taxes on unhealthy foods, restricting unhealthy food promotion and front-of-pack labelling. The Food-EPI was found to be a robust tool and process to benchmark governments' progress to create healthy food environments.
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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 | 0.021 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".