An examination of public support for 35 nutrition interventions across seven countries
Bibliographic record
Abstract
BACKGROUND: Public support for evidence-based nutrition interventions can be an important determinant of government willingness to develop and implement such interventions. The aim of this study was to assess support for a broad range of nutrition interventions across seven countries: Australia, Canada, China, India, New Zealand, the United Kingdom, and the United States. Assessed interventions included those relating to food availability, affordability, reformulation, labelling, and promotion. METHODS: Approximately 1000 adults per country (total n = 7559) completed an online survey assessing support for 35 nutrition interventions/policies. ANOVA analyses were used to identify differences between countries on overall levels of support and by intervention category. Multiple regression analyses assessed demographic and diet-related factors associated with higher levels of support across the total sample and by country. RESULTS: Substantial levels of public support were found for the assessed interventions across the seven countries and five intervention categories. The highest levels were found in India (Mean across all interventions of 4.16 (standard deviation (SD) 0.65) on a 5-point scale) and the lowest in the United States (Mean = 3.48, SD = 0.83). Support was strongest for interventions involving food labelling (Mean = 4.20, SD = 0.79) and food reformulation (Mean = 4.17, SD = 0.87), and weakest for fiscal interventions (Mean = 3.52, SD = 1.06). Consumer characteristics associated with stronger support were higher self-rated health, higher educational attainment, female sex, older age, and perceptions of consuming a healthy diet. CONCLUSION: The results indicate substantial support for a large range of nutrition interventions across the assessed countries, and hence governments could potentially be more proactive in developing and implementing such initiatives.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".