Socio-Demographic and Lifestyle-Related Characteristics Associated with Self-Reported Any, Daily and Occasional Smoking during Pregnancy
Bibliographic record
Abstract
Smoking during pregnancy is a risk factor for various adverse birth outcomes. In order to develop effective interventions, insight is needed into the characteristics associated with pregnant women who smoke. Unknown is whether these characteristics differ for women who smoke daily and women who smoke occasionally. Our study sample, drawn from the DELIVER study (Sept 2009-March 2011), consisted of 6107 pregnant women in primary care in the Netherlands who were up to 34 weeks pregnant. The associations of thirteen socio-demographic or lifestyle-related characteristics with 'any smoking', 'daily smoking' and 'occasional smoking' during pregnancy were tested using multiple binary logistic regression with general estimating equations (GEE). Characteristics most strongly associated with any smoking were low education (OR 10.3; 95% confidence interval (CI) 7.0-15.4), being of Turkish ethnicity (OR 3.9; 95%CI 2.3-6.7) and having no partner (OR 3.7; 95%CI 2.3-6.0). Women of Dutch ethnicity were three times more likely to smoke than those from Dutch-speaking Caribbean countries and non-religious women were much more likely to smoke than religious women. Low education was markedly more strongly associated with daily smoking than with occasional smoking (OR 20.3; 95%CI 13.2-31.3 versus OR 6.0; 95%CI 3.4-10.5). Daily smokers were more likely to be associated with other unfavorable lifestyle-related characteristics, such as not taking folic acid, being underweight, and having had an unplanned pregnancy. There is still much potential for health gain with respect to smoking during pregnancy in the Netherlands. Daily and occasional smokers appear to differ in characteristics, and therefore possibly require different interventions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".