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Record W2010167180 · doi:10.1016/j.ypmed.2015.04.012

Effect of smoke-free patio policy of restaurants and bars on exposure to second-hand smoke

2015· article· en· W2010167180 on OpenAlexaffabout
Sunday Azagba

Bibliographic record

VenuePreventive Medicine · 2015
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsUniversity of WaterlooImpact
Fundersnot available
KeywordsSmokeMedicineSecondhand smokeEnvironmental healthSmoking banNova scotiaTobacco smokeLegislationPassive smokingLawEngineeringGeographyWaste management

Abstract

fetched live from OpenAlex

OBJECTIVE: While there is increasing support for restricting smoking in restaurant and bar patios, there is limited evidence on the effectiveness of this policy. This study examined the effect of smoke-free patio policy of restaurants and bars on adult second-hand smoke (SHS) exposure. METHODS: Data were drawn from the 2005-2012 Canadian Tobacco Use Monitoring Survey (n=89,743), a repeated cross-sectional survey of youth and adult. Regression analysis, a quasi-experimental design was used to examine the effect of provincial smoke-free patio policy on self-reported exposure to SHS. RESULTS: Analyses suggest that exposure to SHS on patios of bars and restaurants declined following the adoption of provincial smoke-free patio policy. Relative to pre-policy SHS exposure, regression results showed a reduction in the probability of SHS exposure of up to 25% in Alberta. Similarly, in Nova Scotia, the probability of SHS exposure declined by up to 21%. Analyses stratified by smoking status found similar significant effect on both smokers and non-smokers. CONCLUSIONS: Findings suggest that provincial patio smoking ban on bars and restaurants had the intended effect of protecting non-smokers from SHS exposure. This study is consistent with a large body of evidence showing that a strong smoke-free legislation is an effective public health measure.

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)0.001
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 score0.087
Threshold uncertainty score0.463

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.037
GPT teacher head0.351
Teacher spread0.314 · 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

Citations23
Published2015
Admission routes2
Has abstractyes

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