Does Believing Alcohol Causes Cancer Moderate the Relationship Between Consumer Awareness of the Alcohol–Cancer Link and Support for Alcohol Policies? Findings From a <scp>Canadian</scp> Cross‐Sectional Study
Bibliographic record
Abstract
INTRODUCTION: Extending research observing an association between awareness that alcohol causes cancer and support for alcohol policies, this study examined if believing or accepting alcohol causes cancer moderates the relationship between awareness of alcohol as a carcinogen and policy support. METHODS: Adult alcohol consumers (n = 5180) in Canada completed an online survey in March-April 2023. Four separate logistic regression models were conducted with policy support affecting alcohol availability, pricing, marketing and labelling as outcomes to assess if believing alcohol causes seven types of cancer moderates the relationship between awareness of the alcohol-cancer link and support for alcohol policies. An interaction between awareness and belief was included as a predictor, adjusting for covariates. RESULTS: Overall, 29.3% were aware alcohol causes seven types of cancer and, of those aware, 83.6% believed this link. Those both aware of and believing that alcohol causes cancer had higher odds of supporting policies restricting alcohol availability (OR 1.76, 95% CI 1.13, 2.74) and marketing (OR 1.75, 95% CI 1.16, 2.64) than those not aware and did not believe. Consumers who were both aware of and believed the alcohol-cancer link had higher odds of supporting labelling policies (OR 1.59, 95% CI 1.05, 2.40), although this was not significant after adjusting for multiple comparisons. DISCUSSION AND CONCLUSIONS: This study highlights that believing alcohol is a carcinogen moderates the relationship between awareness of the alcohol-cancer link and support for policies restricting alcohol availability and marketing. Future longitudinal studies are needed to test interventions for effectively raising awareness and strengthening belief and acceptance of alcohol-related cancer risks.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".