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Record W4401967823 · doi:10.1177/10105395241273250

Expansion of Smoke-Free Laws in Public Places and Support for Smoke-Free in Malaysia: Findings from the 2020 ITC Malaysia Survey

2024· article· en· W4401967823 on OpenAlexaff
Shiz Yee Gan, Farizah Mohd Hairi, Mahmoud Danaee, Amer Siddiq Amer Nordin, Anne C K Quah, Susan Kaai, Mi Yan, Geoffrey T. Fong

Bibliographic record

VenueAsia Pacific Journal of Public Health · 2024
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsOntario Institute for Cancer ResearchUniversity of Waterloo
Fundersnot available
KeywordsEnvironmental healthEnforcementSmokeTobacco controlPassive smokingEntertainmentPublic healthTobacco smokeMedicineGeographyPolitical scienceLawPathology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.669

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.083
GPT teacher head0.336
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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