Prevention and treatment of smoking and tobacco use during pregnancy in selected indigenous communities in high-income countries of the United States, Canada, Australia, and New Zealand: an evidence-based review
Bibliographic record
Abstract
Tobacco smoking during pregnancy is the most important modifiable risk factor for adverse pregnancy outcomes and long-term health complications for mother and baby. Tobacco use during pregnancy has decreased in high-income countries but not in Indigenous women in Australia, New Zealand, the United States, and Canada. This evidence-based review focuses on tobacco use among Indigenous pregnant women in high-income countries that share a history of European colonization. Indigenous women are more likely to use tobacco because of socioeconomic disadvantage, social norms, and poor access to culturally appropriate tobacco cessation support. Complications arising from tobacco smoking during pregnancy, such as low birth weight, prematurity, perinatal death, and sudden infant death syndrome, are much higher in Indigenous populations. Effective approaches to cessation in pregnant nonindigenous women involves behavioral counseling, with or without nicotine replacement therapy (NRT). Higher nicotine metabolism during pregnancy and poor adherence may affect therapeutic levels of NRT. Only two randomized trials were conducted among Indigenous women: neither found a statistically significant difference in cessation rates between the treatment and comparison arms. Considerations should be given to (1) whole life course approaches to reduce tobacco use in Indigenous women, (2) prohibiting tobacco promotion and reducing access to alcohol for minors to prevent smoking initiation in Indigenous youth, and (3) training health-care professionals in culturally appropriate smoking cessation care to improve access to services. It is critical to ensure acceptability and feasibility of study designs, consult with the relevant Indigenous communities, and preempt implementation challenges. Research is needed into the effect of reducing or stopping smoking during pregnancy when using NRT on subsequent maternal and infant outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".