A pre-post evaluation of and public support for smoke-free policies at the 2016 Rio Olympics: findings from the ITC Brazil survey, 2012 - 17
Bibliographic record
Abstract
Background Large-scale international sporting events such as the Olympic Games provide opportunities for hosting nations to promote smoke-free environments. The Olympics have been tobacco-free since 1998, but there have been few studies on the effectiveness of smoke-free Olympic policies. This study evaluated the effectiveness of smoke-free policies at the 2016 Rio Olympics, which were supported by Brazil's 2014 comprehensive smoke-free law, and measured public support for smoke-free Olympic venues. Methods Data were from Waves 2-3 of the ITC Brazil Survey, a cohort survey of approximately 1200 adult smokers and 600 non-smokers in Rio de Janeiro, São Paulo, and Porto Alegre. Wave 2 (2012-13), conducted before the 2014 smoke-free law and 2016 Olympics provided the pre-policy data, and Wave 3 (2016-17) provided the post-policy data. Analyses employed GEE logistic regression models. Results Of 1826 respondents surveyed in 2016-17, 116 (9.9%) smokers and 57 (14.8%) non-smokers attended the Olympics. The vast majority who attended at least one indoor event reported that smoking was banned inside venues (97.5% smokers, 93.0% non-smokers); and noticed no-smoking signs inside venues (82.2% smokers, 73.1% non-smokers). Only 10.7% of smokers and 7.2% of non-smokers noticed people smoking inside venues; 5.8% of smokers said they personally smoked at an indoor venue. Following the 2014 smoking ban and 2016 Olympics, support for the indoor smoking ban in Olympic venues increased among smokers (79.2% to 93.3%, p< .001) and non-smokers (86.4% to 94.1%, p< .001). Conclusions There was strong implementation of and high compliance with Brazil's comprehensive smoking ban at the 2016 Rio Olympics. Smoke-free Olympic venues were widely supported by the public before the Games, and increased after the Games. It would be beneficial for the upcoming 2020 Tokyo Olympics organizers to consult with Rio Olympics organizers to maximize the effectiveness of any efforts to make the Tokyo Olympics smoke-free.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".