Evaluation of the smoking ban in public places in France one year and five years after its implementation: Findings from the ITC France survey.
Bibliographic record
Abstract
France implemented a comprehensive smoke-free policy in public places in February 2007 for workplaces, shopping centres, airports, train stations, hospitals and schools. On January 2008, it was extended to meeting places (bars, restaurants, hotels, casinos, nightclubs). This paper evaluates France's smoke-free law based on the International Tobacco Control Policy Evaluation Project in France (the ITC France Project), which conducted a cohort survey of approximately 1,500 smokers and 500 non-smokers before the implementation of the laws (Wave 1, conducted December 2006 to February 2007) and two waves after the implementation (Wave 2, conducted between September-November 2008; and Wave 3, conducted between September-December 2012). Results show that the smoke-free law led to a very significant and near total elimination of indoor smoking in key venues such as bars (from 95.9% to 3.7%) and restaurants (from 64.7% to 2.3%) at Wave 2, which was sustained four years later at Wave 3 (1.4% in restaurants; 6.6% in bars). Smoking in workplaces declined significantly after the law (from 42.6% to 19.3%), which continued to decline at Wave 3 (to 12.8%). Support for the smoke-free law increased significantly after their implementation and continued to increase at Wave 3 (among smokers for bars and restaurants; among smokers and non-smokers for workplaces). The findings demonstrate that smoke-free policies that are implemented in ways consistent with the Guidelines for Article 8 of the WHO Framework Convention on Tobacco Control (WHO FCTC) lead to substantial and sustained reductions in tobacco smoke in public places while also leading to high levels of support by the public.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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) | 0.001 | 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".