The <scp>INFORMAS</scp> healthy food environment policy index (<scp>Food‐EPI</scp>) in <scp>M</scp>exico: <scp>A</scp>n assessment of implementation gaps and priority recommendations
Bibliographic record
Abstract
Mexico is one of the countries with the highest prevalence of obesity and recently declared a national epidemic of diabetes. Healthy food environments have the potential to improve the diet of the population and decrease the burden of disease. The aim of the study was to assess the efforts of the Mexican Government towards creating healthier food environments using the Healthy Food Environment Policy Index (Food-EPI). The tool was developed by the International Network for Food and Obesity/Non-communicable Diseases Research, Monitoring and Action Support (INFORMAS). Then, it was adapted to the Latin-American context and assessed the components of policy and infrastructure support. Actors from academia, civil society, government, and food industry assessed the level of implementation of food policies compared with international best practices. Actors were classified as (1) independents from academia and civil society (n = 36), (2) government (n = 28), and (3) industry (n = 6). The indicators with the highest percentage of implementation were those related to monitoring and intelligence. Those related to food retail were rated lowest. When stratified by type of actor, the government officials rated several indicators at a higher percentage of implementation compared with independent actors. None of the indicators were rated at high implementation. Government officials and independent actors agreed upon nine priority actions to improve the food environment in Mexico. These actions have the potential to improve government commitment and advocacy efforts to create healthier 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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".