Public support for unhealthy food marketing policies in Australia: A cross-sectional analysis of the International Food Policy Study 2022
Bibliographic record
Abstract
OBJECTIVE: This study aimed to explore public opinion towards food marketing policies. METHODS: In 2022, a cross-sectional online survey was completed by 3,923 adults in Australia, including 1,152 caregivers of children aged <18 years. Concern about children's exposure to unhealthy food marketing was assessed among caregivers. Public support for seven policy options to restrict unhealthy food marketing in different media and settings (broadcast, online, outdoors, packaging and retail) was quantified. Multivariable regression analyses were conducted to examine sociodemographic differences. RESULTS: Most caregivers (85%) reported some degree of concern about their child's exposure to unhealthy food marketing. Among all respondents, there was a high level of support or neutrality (>70%) for all policies aimed at restricting unhealthy food marketing. Respondents who were female, older, highly educated, who identified as Aboriginal and/or Torres Strait Islander, perceived their monthly income as adequate or had at least one child living in the household reported higher support/neutrality towards several of the assessed policies. CONCLUSIONS: Most Australian adults were supportive or neutral towards policies restricting unhealthy food marketing. The level of support varied depending on the policy's target group and its setting. IMPLICATIONS FOR PUBLIC HEALTH: Implementing unhealthy food marketing policies in Australia would most likely have broad public support and minimal opposition.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".