Conhecimento e uso de cigarros eletrônicos e percepção de risco no Brasil: resultados de um país com requisitos regulatórios rígidos
Bibliographic record
Abstract
Given the uncertainties regarding electronic cigarettes' (e-cigs) impact on health, in 2009 Brazil prohibited sales, importation or advertisements of these products until manufacturers are able to show they are safe and/or effective in smoking cessation. This study sought to analyze: (1) awareness of electronic cigarettes, ever-use and recent use; (2) perception of harmfulness of electronic cigarettes when compared with conventional cigarettes; and (3) correlates of awareness and perception of harmfulness. This is a cross-sectional study among Brazilian smokers (≥ 18 years) using the Wave 2 replenishment sample of the Brazilian International Tobacco Control Policy Evaluation Survey. Participants were recruited in three cities through a random-digit dialing sampling frame between October 2012 and February 2012. Among the 721 respondents, 37.4% (n = 249) of current smokers were aware of e-cigs, 9.3% (n = 48) reported having ever tried or used e-cigs and 4.6% (n = 24) reported having used them in the previous six months. Among those who were aware of e-cigs, 44.4% (n = 103) believed they were less harmful than regular cigarettes (low perception of harmfulness). "Low perception of harmfulness" was associated with a higher educational level and with having recently tried/used e-cigs. Despite restrictions to e-cigs in Brazil, 4.6% of sample smokers reported having recently used them. Health surveillance programs in Brazil and other countries should include questions on use and perceptions of e-cigs considering their respective regulatory environments.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| 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".