Evaluating an Association Between Prenatal Smoking Behavior and Exclusive Breastfeeding: A Population-Based Study
Bibliographic record
Abstract
Background: Prenatal smoking is consistently associated with adverse breastfeeding outcomes. We aimed to evaluate the association of smoking cessation or continuation during pregnancy with exclusive breastfeeding in a representative sample of the general Canadian population. Methods: We used the pooled sample of 9860 females with pregnancy experience of last under-five child from the Canadian Community Health Surveys 2015-2018 public use microdata file. We categorized self-reported prenatal smoking status as continuing, quitting, or no smoking. We evaluated the association between exclusive breastfeeding for 6 months or more with prenatal smoking status using multivariable logistic regression, adjusted for socio-demographic variables. Results: With the pooled prevalence of 33.2% (95% CI 31.7, 34.8), 34.4% (95% CI 32.8, 36.1) of non-smokers, 25.7% (95% CI 20.2, 32.2) of those who quit and 15.7% (95% CI 10.8, 22.2) of those who continued smoking reported exclusive breastfeeding for 6 months or more. Continuing smoking had lower odds of exclusive breastfeeding (aOR .47; 95% CI 0.30,0.75) but quitting smoking had no difference (aOR .78;95% CI 0.56,1.08) when compared to non-smokers. Conclusion: Continuing smoking during pregnancy was associated with lower rates of exclusive breastfeeding of infants for 6 months or more. Smoking cessation interventions during prenatal visits may improve exclusive breastfeeding rates.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".