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Record W2066590718 · doi:10.1097/jom.0b013e31819cb222

Impact of an Indoor Smoking Ban on Bar Workers’ Exposure to Secondhand Smoke

2009· article· en· W2066590718 on OpenAlexaffabout
Susan J. Bondy, Bo Zhang, Nancy Kreiger, Peter Selby, Neal L. Benowitz, Heather E. Travis, Ana Florescu, Nicole Greenspan, Roberta Ferrence

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2009
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsUniversity of Toronto
FundersUniversity of California, San Francisco
KeywordsEnvironmental healthSecondhand smokeWindsorSmoking banMedicineSmokeCotinineEngineeringEnvironmental scienceNicotineWaste management

Abstract

fetched live from OpenAlex

OBJECTIVE: To evaluate the impact of an indoor smoke-free bylaw in Toronto, Ontario, implemented June 2004. METHODS: We used a pre-post comparison design to assess secondhand smoke (SHS) exposure among 79 eligible bar workers in Toronto, Ontario (bylaw enacted), and 49 eligible bar workers in a control community, Windsor, Ontario (no bylaw change), at four times: preban, and 1, 2, and 9 months postban. RESULTS: SHS exposure time and urinary cotinine level were substantially reduced in Toronto bar workers immediately after the ban by 94% (from 7.8 to 0.5 hours) and 68% (from 24.2 to 7.8 ng/mL), respectively. The reduction was sustained throughout follow-up. There was no change among Windsor bar workers before and after the ban. CONCLUSIONS: Compliance with the ban was high, and the ban led to a substantial reduction in SHS exposure.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.655
Threshold uncertainty score0.695

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.035
GPT teacher head0.336
Teacher spread0.301 · 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 source (direct Gemma or distilled Codex), 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

Citations29
Published2009
Admission routes2
Has abstractyes

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