Public Support for Nutrition-Related Actions by Food Companies in Australia: A Cross-Sectional Analysis of Findings from the 2020 International Food Policy Study
Bibliographic record
Abstract
Unhealthy food environments contribute to unhealthy population diets. In Australia, the government currently relies on voluntary food company actions (e.g., related to front-of-pack labelling, restricting promotion of unhealthy foods, and product formulation) as part of their efforts to improve population diets, despite evidence that such voluntary approaches are less effective than mandatory policies. This study aimed to understand public perceptions of potential food industry nutrition-related actions in Australia. An online survey was completed by 4289 Australians in 2020 as part of the International Food Policy Study. The level of public support was assessed for six different nutrition-related actions related to food labelling, food promotion, and product formulation. High levels of support were observed for all six company actions, with the highest support observed for displaying the Health Star Rating on all products (80.4%) and restricting children's exposure to online promotion of unhealthy food (76.8%). Findings suggest the Australian public is strongly supportive of food companies taking action to improve nutrition and the healthiness of food environments. However, given the limitations of the voluntary action from food companies, mandatory policy action by the Australian government is likely to be needed to ensure company practices align with public expectations.
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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 | 0.003 | 0.007 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".