Pictorial health warning label content and smokers' understanding of smoking-related risks--a cross-country comparison
Bibliographic record
Abstract
The aim of the present study was to assess smokers' level of agreement with smoking-related risks and toxic tobacco constituents relative to inclusion of these topics on health warning labels (HWLs). 1000 adult smokers were interviewed between 2012 and 2013 from online consumer panels of adult smokers from each of the three countries: Australia (AU), Canada (CA) and Mexico (MX). Generalized estimating equation models were estimated to compare agreement with smoking-related risks and toxic tobacco constituents. For disease outcomes described on HWLs across all three countries, there were few statistical differences in agreement with health outcomes (e.g. emphysema and heart attack). By contrast, increases in agreement where the HWLs were revised or introduced on HWLs for the first time (e.g. blindness in AU and CA, bladder cancer in CA). Similarly, samples from countries that have specific health content or toxic constituents on HWLs showed higher agreement for that particular disease or toxin than countries without (e.g. higher agreement for gangrene and blindness in AU, higher agreement for bladder cancer and all toxic constituents except nitrosamines and radioactive polonium in CA). Pictorial HWL content is associated with greater awareness of smoking-related risks and toxic tobacco constituents.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".