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Record W2220906113 · doi:10.24095/hpcdp.34.2/3.06

Are Canadian youth still exposed to second-hand smoke in homes and in cars?

2014· article· en· W2220906113 on OpenAlexaffvenueabout
Andriana Barisic, ST Leatherdale, Robin Burkhalter, Miljenko Math, Rashid Ahmed

Bibliographic record

VenueChronic diseases and injuries in Canada · 2014
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsImpactCancerCare ManitobaCanadian Cancer SocietyUniversity of WaterlooCancer Care Ontario
Fundersnot available
KeywordsEnvironmental healthLogistic regressionSmokeSecondhand smokeDescriptive statisticsMedicineDemographyEngineeringMathematicsSociologyWaste managementStatistics

Abstract

fetched live from OpenAlex

INTRODUCTION: The objective of this manuscript is to examine the prevalence of youth exposed to second-hand smoke (SHS) in homes and cars, changes in SHS exposure over time, and factors associated with beliefs youth hold regarding SHS exposure among a nationally representative sample of Canadian youth. METHODS: Descriptive analysis of SHS exposure in homes and cars was conducted using data from the Canadian Youth Smoking Survey (2004, 2006 and 2008). Logistic regression was conducted to examine factors associated with beliefs youth had about SHS exposure in 2008. RESULTS: In 2008, 21.5% of youth reported being exposed to SHS in their home on a daily or almost daily basis, while 27.3% reported being exposed to SHS while riding in a car at least once in the previous week. Between 2004 and 2008, the prevalence of daily SHS exposure in the home and cars decreased by 4.7% and 18.0% respectively. CONCLUSION: Despite reductions in SHS exposure over time, a substantial number of Canadian youth continue to be exposed to SHS in homes and cars. Further effort is required to implement and evaluate policies designed to protect youth from SHS.

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)0.001
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 score0.021
Threshold uncertainty score0.152

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.014
GPT teacher head0.244
Teacher spread0.230 · 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

Citations4
Published2014
Admission routes3
Has abstractyes

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