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Record W1571872572 · doi:10.24095/hpcdp.29.3.05

Factors associated with the adoption of a smoking ban in Quebec households

2009· article· en· W1571872572 on OpenAlexaffvenueabout
É. Ouedraogo, Fernand Turcotte, M. J. Ashley, Joan M. Brewster, Roberta Ferrence

Bibliographic record

VenueChronic diseases in Canada · 2009
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsCentre for Addiction and Mental HealthOntario Tobacco Research UnitUniversity of TorontoUniversité Laval
Fundersnot available
KeywordsMedicineEnvironmental healthSmoking banNova scotiaLogistic regressionSecondhand smokeTobacco useSmokeHealth promotionPromotion (chess)Passive smokingCross-sectional studyPublic healthGeographyPopulation

Abstract

fetched live from OpenAlex

The home represents an important source of exposure to environmental tobacco smoke for non-smokers, including children, who live with smokers. Our goal is to identify the sociodemographic factors associated with the adoption of smoking bans in "smoker households" in Quebec. Selected associations are compared with three other Canadian provinces (Ontario, British Columbia and Nova Scotia). This is a cross-sectional study involving 2648 respondents. Logistic regression analysis is employed. Few smoker households in Quebec (21%) have a ban on smoking; the presence of a non-smoker is strongly linked to the existence of such a ban; the presence of a child under the age of 6 is less strongly associated with the adoption of a ban in Quebec than in the other provinces, and the presence of an adolescent shows no association whatsoever. In addition to the child health benefits of household smoking bans, greater emphasis should be placed on the impact that such bans can have on children's future smoking behaviour. One option from a health promotion standpoint might be to organize a campaign aimed at non-smokers who live with smokers, in order to urge them to be less tolerant of environmental tobacco smoke.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.107
Threshold uncertainty score0.970

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.025
GPT teacher head0.253
Teacher spread0.228 · 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

Citations5
Published2009
Admission routes3
Has abstractyes

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