Home environmental tobacco smoke exposure in Canadian children
Bibliographic record
Abstract
BACKGROUND: Children living with a smoker experience increased environmental tobacco smoke (ETS) exposure, even when the smoker refrains from smoking inside the house, compared to children not living with a smoker. Given the risks of ETS in children, it was hypothesized that households with children are less likely than those without children to experience home ETS exposure. DESIGN: Cross-sectional analysis of the Canadian Community Health Survey 2009-2010 for the association between children aged <12 years in the household and home ETS exposure, using logistic regression and considering household education and income as confounders or effect modifiers. A subgroup analysis was conducted comparing younger child households (at least one child aged <6 years) to older child households (only children aged 6-11 years). RESULTS: Of 66 631 households included, home ETS exposure occurred in 25% of households without children and 22% of households with children. Households with children were less likely than those without children to experience ETS exposure (OR 0.83, 95%CI 0.80-0.87). Effect modification by education and income was observed. No difference was observed in ETS exposure between older child and younger child households (OR 0.98, 95%CI 0.91-1.05). CONCLUSION: Households with children are marginally less likely than households without children to experience home ETS exposure.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".