Improving Knowledge that Alcohol Can Cause Cancer is Associated with Consumer Support for Alcohol Policies: Findings from a Real-World Alcohol Labelling Study
Bibliographic record
Abstract
Knowledge that alcohol can cause cancer is low in Canada. Alcohol labels are one strategy for communicating alcohol-related harms, including cancer. Extending existing research observing an association between knowledge of the alcohol-cancer link and support for alcohol policies, this study examined whether increases in individual-level knowledge that alcohol is a carcinogen following an alcohol labelling intervention are associated with support for alcohol polices. Cancer warning labels were applied to alcohol containers at the intervention site, and the comparison site did not apply cancer labels. Pre-post surveys were conducted among liquor store patrons at both sites before and two-and six-months after the intervention was stopped due to alcohol industry interference. Limiting the data to participants that completed surveys both before and two-months after the cancer label stopped, logistic regression was used to examine the association between increases in knowledge and support for policies. Support for pricing and availability policies was low overall; however, increases in individual-level knowledge of the alcohol-cancer link was associated with higher levels of support for pricing policies, specifically, setting a minimum unit price per standard drink of alcohol (OR = 1.86, 95% CI: 1.11-3.12). Improving knowledge that alcohol can cause cancer using labels may increase support for alcohol policies. International Registered Report Identifier (IRRID): RR2-10.2196/16320.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".