Public support for food policies in Mexican adults: Findings from the International Food Policy Study, 2017–2021
Bibliographic record
Abstract
Deaths attributable to unhealthful eating underscore the need to improve dietary patterns through upstream, policy-led solutions. The approval and successful implementation of food policies is partly determined by their public acceptance. Little is known about public support for food policies in Mexico. We aimed to investigate the level of public support for 30 food policies, grouped into 5 domains, and their associated characteristics among Mexican adults. Data are from the 2017-2021 International Food Policy Study (IFPS), a cross-sectional web-based survey of adults. Differences in public support across years were estimated using linear regression models. The association between demographic characteristics and policy support was analyzed using multivariate logistic regression models. The highest mean support was for the subsidies and benefits domain, followed by the labelling and reformulation domain. The level of support varied across years and policy domains. Support was higher in 2019 compared to 2017 and 2018, and subsequently lower in 2020 and 2021 compared to previous years. Older age was associated with greater support across all domains (OR ranged from 0.002 to 0.004, p < 0.01). Female participants and those selfidentifying as indigenous showed greater support for certain policy domains, whereas higher income adequacy was associated with lower support for other policy domains. In Mexico, support for food policies varies across subpopulations. Our findings may serve as a guide to the development and promotion of food policies in Mexico, as well as to improve their feasibility and success.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".