Prevalence and Correlates of Smoking During Pregnancy: A Comparison of Aboriginal and Non‐Aboriginal Women in Manitoba
Bibliographic record
Abstract
BACKGROUND: Prenatal smoking rates vary substantially among racial and ethnic groups. Although prevalence of smoking among Aboriginal people in Canada is higher than in the general population, little is known about smoking rates during pregnancy among Aboriginal women or the characteristics of Aboriginal women more likely to smoke during pregnancy. The study purpose was to describe and compare the prevalence and correlates of smoking during pregnancy among Aboriginal and non-Aboriginal women giving birth in the Canadian province of Manitoba. METHODS: Data were obtained from interviews with 684 postpartum women who delivered a live singleton infant in two tertiary hospitals in Manitoba. Stratified analysis was used to describe effect-measure modification for correlates of smoking among the Aboriginal and non-Aboriginal groups. A multivariable logistic regression was conducted for the total sample. RESULTS: A significantly higher proportion of Aboriginal women (61.2%) than non-Aboriginal women (26.2%) smoked during pregnancy. No correlates of smoking during pregnancy were specific to Aboriginal women, but several maternal characteristics were associated with smoking among non-Aboriginal women. After controlling for other factors, significant correlates of smoking during pregnancy for the total sample included inadequate prenatal care, low support from others, single marital status, illicit drug use, Aboriginal race/ethnicity, and noncompletion of high school among non-Aboriginal women. CONCLUSIONS: The high prevalence of smoking during pregnancy, particularly among Aboriginal women, necessitates coordinated efforts aimed at smoking prevention and cessation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".