Effectiveness of the ban on tobacco industry sponsorship in Brazil: findings from the ITC Brazil Wave 1 to 3 Surveys (2009 to 2016 - 17)
Bibliographic record
Abstract
Background In 2000, Brazil implemented a comprehensive ban on tobacco advertising (except point-of-sale), promotion, and sponsorship, including sponsorship by tobacco brands of national sporting and arts events, then in 2005 adding international sport events. However, a loophole in the law permits company names for sponsorship. This study examines whether this loophole has been exploited by the industry via Brazilians' awareness of tobacco company sponsorship of sporting and arts events between 2009 and 2016-17. Methods Data were from Waves 1 to 3 (2009, 2012-13, 2016-17) of the International Tobacco Control (ITC) Brazil Survey - a cohort survey of approximately 1200 adult smokers and 600 non-smokers in Rio de Janeiro, São Paulo, and Porto Alegre. At all three waves, respondents were asked whether they had seen or heard about (1) sport or sporting events and/or (2) music, theatre, art or fashion events sponsored by or connected with cigarette companies. At Waves 2 and 3, tobacco company name recognition was tested by asking respondents whether the following are tobacco companies: Souza Cruz, Nestle, and Pirelle. Data were analyzed using GEE logistic regression models. Results Awareness of a sport or sporting event sponsored by a cigarette company decreased between 2009 and 2016-17 among both smokers (from 8.7% to 4.7%; p=0.004) and non-smokers (from 11.8% to 5.7%; p=0.008). Awareness of an arts event sponsored by a tobacco company decreased among smokers (from 6.3% to 1.4%; p< .001), but not significantly among non-smokers (from 6.1% to 3.8%; p=0.235). Over 93% of smokers and non-smokers correctly stated that Souza Cruz is a tobacco company and over 98% correctly stated that Nestle and Pirelle are not. Conclusions Although Brazil has not specifically banned tobacco company sponsorship, public awareness of tobacco company sponsorship has decreased over 7 years, possibly suggesting that the industry has not (yet) taken advantage of the loophole.
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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 | 0.013 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".