The impact of smoke-free legislation on educational differences in birth outcomes
Bibliographic record
Abstract
BACKGROUND: Smoke-free legislation may have positive effects on birth outcomes. Given that smoking and secondhand smoke during pregnancy vary with socioeconomic position, legislation may have greater effects in some socioeconomic groups. For this study, we evaluated the impact of a 2006 ban on smoking in public places in the Canadian province of Quebec on preterm birth, small-for-gestational-age birth and birth weight, and on educational differences in these birth outcomes. METHODS: We analysed data on singleton births in Quebec between 2003 and 2010. Logistic regression was used to model the association of smoke-free legislation with preterm birth (<37 weeks), very preterm birth (<34 weeks), small-for-gestational-age birth (<10th centile for gestational age and sex), low birth weight (<2500 g) and mean birth weight, adjusting for secular trends before and after legislation. Interaction terms were included to assess differential effects by level of maternal education. RESULTS: Smoke-free legislation was associated with average reductions of 3.1 preterm births (95% CI 0.1 to 6.0), 2.3 very preterm births (95% CI 0.9 to 3.7), 5.9 small-for-gestational-age births (95% CI 2.6 to 9.3) and 1.0 low birthweight infants (95% CI 0.4 to 1.6) per 1000 live births, as well as a 17.1 g increase in mean birth weight (95% CI 10.7 to 23.6). Legislation was associated with improved birth outcomes in all categories of maternal education. CONCLUSIONS: Smoke-free legislation in Quebec was associated with reductions in preterm and small-for-gestational-age births, and an increase in birth weight. There was no compelling evidence that legislation impacted educational gradients in birth outcomes.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".