Brazilian smokers are ready for the ban on flavour additives in tobacco to be implemented
Bibliographic record
Abstract
Brazil became the first country to approve a national policy to ban all flavour additives in all tobacco leaf products in 2012. However, as of February 2022, the policy remained to be implemented. Cross-sectional data come from the International Tobacco Control (ITC) Brazil Wave 3 Survey among adult smokers (N = 1216) in 2016-2017. The majority of smokers supported a ban on menthol (56.0%; 95%CI: 51.7-60.2%) and a ban on all additives (61.7%; 57.5-65.8%), with no significant differences across sociodemographic groups in adjusted logistic regression models. More than half of menthol smokers reported they would either quit or reduce the amount they smoked if menthol cigarettes were banned. Findings suggest that there is support for Brazil's ban on flavour additives, which is a determinant of successful policy implemented. Continued delays will postpone an important measure with demonstrated public health gains.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.009 | 0.001 |
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".