Associations of Quantity Smoked and Socioeconomic Status With Smoke-Free Homes and Cars Among Daily Smokers
Bibliographic record
Abstract
INTRODUCTION: An association between socioeconomic status (SES) and smoke-free private spaces among smokers could be due to heavier smoking among low SES smokers. We assessed whether quantity smoked or SES are independently associated with smoke-free homes or cars in daily smokers. METHOD: Data were drawn from a cross-sectional telephone survey (2011-2012) of 750 daily smokers age ≥18 years in Quebec, Canada (45% response). Multivariable logistic regression was used to model the independent association between (a) number of cigarettes smoked per day, and (b) each of educational attainment, annual household income, or active employment status and smoke-free homes or cars. RESULTS: Participants were 41.0 years old on average, 57% were female. Median (IQR) number of cigarettes smoked per day was 14 (10, 20). Forty-eight percent of participants reported smoke-free homes; 34% reported smoke-free cars. Quantity smoked was strongly associated with both smoke-free homes and cars. Income and education (but not actively employed) were associated with smoke-free homes. None of the SES indicators were associated with smoke-free cars. CONCLUSIONS: Interventions targeting smokers to promote smoke-free homes and cars should incorporate components to help smokers reduce quantity smoked or preferably, to quit. Interventions targeting smoke-free homes will also need to address SES inequalities by education and income. Our data suggest that reduction in quantity smoked may help smokers reduce SHS exposure in cars, but that an inequality lens may not be relevant.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".