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Record W1984084718 · doi:10.1136/ip.2009.025114

The impact of reduced ignition propensity cigarette regulation on smoking behaviour in a cohort of Ontario smokers: Figure 1

2010· article· en· W1984084718 on OpenAlexaffabout
Richard J. O’Connor, Brian V. Fix, David Hammond, Gary A. Giovino, Andrew Hyland, Geoffrey T. Fong, K. Michael Cummings

Bibliographic record

VenueInjury Prevention · 2010
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsOntario Institute for Cancer ResearchUniversity of Waterloo
FundersNational Cancer Institute
KeywordsCohortDemographyMedicineLegislationEnvironmental healthPoison controlInjury preventionCohort studySuicide preventionOccupational safety and healthTelephone surveyCigarette smokingIncidence (geometry)Advertising

Abstract

fetched live from OpenAlex

This study examined the degree to which legislation intended to reduce the incidence of cigarette-caused fires influenced the behaviours of a cohort of smokers in Ontario. A random digit dialled telephone survey of adult smokers residing in Ontario was conducted in 2005, ending 1 month prior to the reduced ignition propensity (RIP) regulation's implementation date. A follow-up survey was conducted one year later. Of the baseline participants, 73.0% (n=435) completed the follow-up survey. The frequency of fire risk behaviours was similar across both surveys. At baseline, only 3.7% of smokers interviewed reported that their cigarettes went out on their own 'often' while smoking. Following the implementation of the reduced ignition propensity legislation, this increased significantly to 14.7%. Results suggest that the proportion of Ontario smokers who reported engaging in behaviour such as leaving a cigarette burning unattended and smoking in bed actually declined, although these declines were not statistically significant across all measures of fire risk.

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)0.000
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 score0.009
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.029
GPT teacher head0.328
Teacher spread0.299 · 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

Citations7
Published2010
Admission routes2
Has abstractyes

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