A Narrative Review of the Efficacy and Design of Safety Labels on Tobacco Products to Promote the Use of Safety Labels on Alcohol Products in Canada
Bibliographic record
Abstract
Alcohol is consumed by approximately three-quarters of Canadians. Alcohol causes acquired liver disease, increases the risk of cancer, has detrimental effects on mental health, and leads to adverse pregnancy outcomes. Alcohol-related morbidity and mortality are high, and urgent public health measures are warranted to prevent and control these. Tobacco safety labels have been shown in numerous studies to reduce tobacco consumption. Much can be learned from the design of tobacco safety labels in creating promising alcohol safety labels that can possibly help reduce alcohol consumption. The aim of this paper is to review the efficacy of tobacco safety labels in reducing tobacco consumption and the design of tobacco safety labels and to propose a promising design for alcohol safety labels based on our findings. English peer-reviewed papers published in western countries since 2000 were searched on PubMed and Google Scholar. Keywords and synonyms were used to search pertinent papers, which were subsequently screened by title and abstract and fully reviewed if relevant. Findings from studies comparing designs of safety labels on alcohol and tobacco products are similar. Graphics, higher emotion content, and greater size are associated with greater attention, awareness, negative emotions, intention to quit, and reduction in consumption. Mixed results are found for testimonials containing safety labels on tobacco products. It is unclear whether testimonials on alcohol safety labels reduce alcohol consumption or not. Safety labels with specific information, such as tobacco-related costs and alcohol-related cancer risks, are more effective in reducing tobacco consumption. In conclusion, preliminary alcohol safety labels show promise. Large safety labels with graphics and high emotional content appear to be most effective and may reduce alcohol consumption.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".