Secondhand Smoke Exposure in Public Places and Support for Smoke-Free Laws in Japan: Findings from the 2018 ITC Japan Survey
Bibliographic record
Abstract
Comprehensive smoke-free policies such as those called for by the WHO FCTC are the only way to protect the public effectively from the harms of secondhand smoke (SHS), yet Japan has been slow to implement this important health measure. This study examines baseline levels of smoking and SHS exposure in public places and support for smoking bans in Japan prior to the implementation of the 2018 national smoke-free law. Data are from the International Tobacco Control (ITC) Japan Wave 1 Survey (Feb–Mar 2018), a web survey of adult cigarette smokers, heated tobacco product users, dual users, and non-users (total N = 4684). Measures included prevalence of smoking (whether respondents noticed people smoking inside restaurants and bars at their last visit, and workplaces in the last month), and support for complete smoking bans in these venues. Smoking prevalence in each venue was high overall in 2018 (49% of workplaces, 55% of restaurants, and 83% of bars), even higher than in China, the country with the greatest toll of SHS. Support for complete smoking bans was very high overall (81% for workplaces, 78% for restaurants, and 65% for bars). Non-users were less likely to be exposed to SHS and had higher support for smoking bans than tobacco users. These findings point to the ineffectiveness of partial smoke-free laws in Japan and reinforce the call for comprehensive smoke-free laws, which even smokers would support at higher levels than in many other ITC countries.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".