Australians support for policy initiatives addressing unhealthy diet: a population-based study
Bibliographic record
Abstract
To inform public health policy implementation in Australia, our study investigated the level of public support for six policy initiatives addressing unhealthy diet. The policy initiatives included taxing soft drinks and energy drinks, taxing less healthy food and beverage purchases, zoning to restrict the supply of junk foods near schools, prohibiting advertising and promotion of less healthy food and beverages to children under the age of 16 and restricting sugar-sweetened beverages from vending machines in schools, and public places. Data from a cross-sectional population-based study for 4040 Australians aged 15+ years, were analysed. A high overall support across all policy initiatives was observed. Nearly three-quarter of public support was observed for policy initiatives targeting children (zoning to restrict the supply of junk food near schools, prohibiting advertising and promotion of less healthy food and beverages to children under the age of 16 and restricting sugars-sweetened beverages from vending machines in schools), and half of Australians supported policy initiatives of taxing soft drinks and energy drinks and taxing less healthy food and beverage purchases. Australian women and those with tertiary level of education were more likely to support public health initiatives targeting children and all policy initiatives respectively. Interestingly, young adults expressed low level of support for all policy initiatives. The study demonstrated considerable public support for policy initiatives focussed on protecting children from unhealthy diet in Australia. Framing, designing and implementing policies targeting children is potentially a good starting point for policymakers to create a health promoting food environment.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".