The prevalence of mandated and voluntary health information on alcohol products in Australia
Bibliographic record
Abstract
OBJECTIVE: Regulations to restrict alcohol promotion and requirements for mandatory display of information about health risks associated with alcohol use have been minimal and hard-won in Australia. This study (i) outlines an approach to monitoring alcohol industry use of health messages on alcohol products and (ii) reports prevalence and nature of government-mandated health-related information and voluntary health messages on alcohol products. METHODS: Images of 5,923 alcohol products sold in four large alcohol stores in Sydney were captured. Data were collected in-store and via web-scraping. Label content was extracted from the images. RESULTS: There was high compliance (97%-99%) with government-mandated requirements other than the pregnancy warning label (63%). Presence of voluntary health-related messages was common (65%), but typically present in the form of DrinkWise (an industry-led social aspects/public relations organisation) statements that are unlikely to be effective. CONCLUSIONS: This study provides a unique and systematic approach to examining alcohol industry compliance with government-mandated on-product information requirements and voluntary inclusion of other health-related messages. IMPLICATIONS FOR PUBLIC HEALTH: The results demonstrate the need for ongoing monitoring to enforce alcohol industry compliance with Australia's existing and future labelling regulations and to assess the industry's voluntary use of other forms of health messaging.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".