“We Have a Right to Know”: Exploring Consumer Opinions on Content, Design and Acceptability of Enhanced Alcohol Labels
Bibliographic record
Abstract
AIMS: This study aimed to refine content and design of an enhanced alcohol label to provide information that best supports informed drinking and to gauge consumer acceptability of enhanced alcohol labels among a subset of consumers. METHODS: Five focus groups (n = 45) were conducted with stakeholders and the general public (age 19+) across one jurisdiction in northern Canada. Interviews were transcribed and analyzed using NVivo software. RESULTS: The majority of participants showed strong support for enhanced alcohol labels with an emphasis on the consumers' right to know about the health risks related to alcohol. Participants preferred larger labels that included standard drink (SD) information, national low-risk drinking guidelines presented as a chart with pictograms, cancer health messaging and a pregnancy warning. Supporting introduction of the labels with a web resource and an educational campaign was also recommended. CONCLUSIONS: Displaying enhanced labels on alcohol containers that include SD information, low-risk drinking guidelines and other health messaging in an accessible format may be an effective way to better inform drinkers about their consumption and increase awareness of alcohol-related health risks. Introduction of enhanced labels shows potential for consumer support. SHORT SUMMARY: Focus group findings indicate strong support for enhanced alcohol labels displaying SD information, national drinking guidelines, health messaging and a pregnancy warning. Introduction of enhanced alcohol labels in tandem with an educational campaign may be an effective way to better inform Canadian drinkers and shows potential for consumer support.
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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 | 0.022 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".