Relationship between constituent labelling and reporting of tar yields among smokers in four countries
Bibliographic record
Abstract
Countries have adopted different approaches to disseminating cigarette tar, nicotine, and carbon monoxide (CO) levels to consumers, with some (e.g. EU member states, Canada, Australia, but not the United States) requiring disclosure of results from the International Organization for Standardization (ISO) test method on packs. Cross-country comparisons can provide insight into how smokers use yields when information is presented differently. We examined whether smokers in four different countries could recall the tar yield of their brand of cigarettes, using data from the third wave of the International Tobacco Control Four Country Survey (ITC-4). Of current smokers in the United States, Canada, Australia and the United Kingdom, 33.6% gave a numeric response when asked to report the tar yield of their brand, whereas 66.4% responded 'I don't know.' American participants (9.2%) were less likely than Canadian (28.0%), UK (36.5%) or Australian (68.2%) smokers to give an answer, even after controlling for sociodemographic and smoking behaviour factors. Constituent labelling policies can affect whether smokers report a tar yield for their cigarette brand. Pack labelling appears to be useful for conveying information about cigarettes to smokers; however, there is an urgent need to develop more meaningful information on toxic constituents of cigarette smoke.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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 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".