Expansion of Smoke-Free Laws in Public Places and Support for Smoke-Free in Malaysia: Findings from the 2020 ITC Malaysia Survey
Bibliographic record
Abstract
Smoke-free laws (SFL) are more effective with public support. This study investigated the smoking prevalence, public perceptions of smoking rules, and support for comprehensive SFL among 1047 people who smoke (PWS) and 206 people who do not smoke (PNS) aged ≥18 in the 2020 International Tobacco Control Malaysia Survey. Smoking prevalence was highest in nighttime entertainment venues (85.7%), non-air-conditioned eateries (49.7%), and indoor workplaces (34.6%). Respondents reported that smoking was banned in most indoor workplaces (81.7% PNS, 69.2% PWS), air-conditioned eateries (84.7% PNS, 75.7% PWS), and non-air-conditioned eateries (81.2% PNS, 78.7% PWS), but much less so in nighttime entertainment venues (30.1% PNS, 24.6% PWS). Support for comprehensive SFL in public venues was highest among PNS (≥84.9%) but still substantial among PWS (≥49.9%). PWS under 40, Malay, married, and aware of smoking rules supported SFL more. Robust SFL enforcement is essential in Malaysia to reduce secondhand smoke exposure in public places.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".