Benchmarking public policies to create healthy food environments compared to best practice: the Healthy Food Environment Policy Index in Guatemala
Bibliographic record
Abstract
BACKGROUND: Benchmarking the implementation of healthy food environment public policies against international best practices may accelerate the government response to prevent obesity and non-communicable diseases (NCDs) in the countries. The aim of the study was to determine the extent of food environment policy implementation in Guatemala and to identify and prioritize actions for the government to accelerate their implementation. METHODS: The INFORMAS Healthy Food Environment Policy Index (Food-EPI from the International Network for Food and Obesity/NCDs Research, Monitoring and Action Support) was used. Evidence of implementation for 50 good practice indicators within the seven food policies and six infrastructure support domains was compiled, and subsequently validated by Guatemalan government officials. A national civil society expert panel on public health and nutrition performed an online assessment of the implementation of healthy food environment policies against best international practices. The level of agreement among evaluators was measured using the Gwet second order agreement coefficient (AC2). The expert panel recommended actions for each indicator during on-site workshops and those actions were prioritized by importance and achievability. RESULTS: The expert panel rated implementation at zero for 26% of the indicators, very low for 28% of indicators, low for 42%, and medium for 4% of indicators (none were rated high). Indicators at medium implementation were related to the use of evidence for developing policies and ingredient list/nutrition information panels on packaged foods. Seventy-seven actions were recommended prioritizing the top 10 for immediate action. The Gwet AC2 was 0.73 (95% CI 0.67-0.80), indicating a good concordance among experts. CONCLUSIONS: In the Food-EPI of Guatemala, almost all indicators of good practice had a low or less level of implementation. The expert panel proposed 12 priority actions to accelerate policy implementation to tackle obesity and NCDs in the country.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".