Home Smoking Bans May Increase the Risk of Smoking Onset in Children When Both Parents Smoke
Bibliographic record
Abstract
INTRODUCTION: Our objective was to determine if there is effect modification by home smoking bans in the association between parental smoking and cigarette smoking onset in children. METHODS: Data on smoking onset, number of parents who smoke, and home smoking rules were collected from children who had never smoked in self-report questionnaires in grades 5, 7, 9, and 11. The association between number of parents who smoke and smoking onset in children was tested in pooled logistic regression in 2 groups defined by the presence or absence of a complete home smoking ban. RESULTS: In homes without a complete ban and relative to participants with no parents who smoke, the odds ratio (95% confidence interval [OR (95% CI]) for smoking onset was 1.5 (1.1-1.9) when one parent smoked and 1.4 (1.0-2.1) when both parents smoked. In homes with a complete ban, the OR (95% CI) was 1.6 (1.1-2.3) if one parent smoked, but 4.9 (2.4-9.9) if both parents smoked. CONCLUSION: The association between number of parents who smoke and smoking onset in children was modified by the presence of a complete home smoking ban. In homes with a complete smoking ban in which both parents smoke, it may be prudent those parents communicate clearly with their children about their reasons for implementing the ban as well as about their reasons for continuing to smoke.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".