Families at Risk: Home and Car Smoking Among Pregnant Women Attending a Low-Income, Urban Prenatal Clinic
Bibliographic record
Abstract
INTRODUCTION: Secondhand smoke exposure (SHSe) has been identified as a distinct risk factor for adverse obstetric and gynecological outcomes. This study examined the prevalence of SHSe reduction practices (i.e., home and car smoking bans) among pregnant women in a large U.S. prenatal clinic serving low-income women. METHODS: Pregnant women (N = 820) attending a university-based, urban prenatal clinic in Houston, Texas, completed a prenatal questionnaire assessing bans on household and car smoking and a qualitative urine cotinine test as part of usual care. Data were collected from April 2011 to August 2012. RESULTS: Nearly one-third (n = 257) of the sample reported at least 1 smoker living in the home. About a quarter of the women in the full sample did not have a total smoking ban in their home and car. Within smoking households, 44% of the pregnant women reported smoking, 56% reported smoking by another household member, and in 26% of smoking households both the pregnant woman and at least one other person were smoking. Only 43% of women with a household smoker reported a total ban on smoking, with higher rates among Hispanic women. Smoking bans were less common when the pregnant women smoked, when more than 1 smoker resided in the home, and when pregnant with her first child. CONCLUSIONS: SHSe among low-income pregnant women is high, and interventions to raise awareness and increase the establishment of smoking bans in homes and cars are warranted.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".