A multi-country survey of public support for food policies to promote healthy diets: Findings from the International Food Policy Study
Bibliographic record
Abstract
BACKGROUND: Poor diet is a significant contributor to the burden of global disease. There are numerous policies available to address poor diets; however, these policies often require public support to encourage policy action. The current study aimed to understand the level of public support for a range of food policies and the factors associated with policy support. METHODS: An online survey measuring support for 13 food policies was completed by 19,857 adults in Australia, Canada, Mexico, the United Kingdom (UK) and the United States (US). The proportion of respondents that supported each policy was compared between countries, and the association between demographic characteristics and policy support was analysed using multivariate logistic regression. RESULTS: The level of support varied between policies, with the highest support for policies that provided incentives (e.g., price subsidies) or information (e.g., calorie labelling on menus), and the lowest support for those that imposed restrictions (e.g., restrictions on sponsorship of sport events). This pattern of support was similar in all countries, but the level differed, with Mexico generally recording the highest support across policies, and the US the lowest. Several demographic characteristics were associated with policy support; however, these relationships varied between countries. CONCLUSION: The results suggest that support for food policies is influenced by several factors related to the policy design, country, and individual demographic characteristics. Policymakers and advocates should consider these factors when developing and promoting policy options.
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".