Smoking cessation in pregnancy: An update for maternitycare practitioners
Bibliographic record
Abstract
INTRODUCTION: This paper provides an up-to-date summary of the effects of smoking in pregnancy as well as challenges and best practices for supporting smoking cessation in maternity care settings. METHODS: We conducted a qualitative review of published peer reviewed and grey literature. RESULTS: There is strong evidence of the effects of maternal tobacco use and secondhand smoke exposure on adverse pregnancy outcomes. Tobacco use is the leading preventable cause of miscarriage, stillbirth and neonatal deaths, and evidence has shown that health effects extend into childhood. Women who smoke should be supported with quitting as early as possible in pregnancy and there are benefits of quitting before the 15th week of pregnancy. There are a variety of factors that are associated with tobacco use in pregnancy (socioeconomic status, nicotine addiction, unsupportive partner, stress, mental health illness etc.). Clinical-trial evidence has found counseling, when delivered in sufficient intensity, significantly increases cessation rates among pregnant women. There is evidence that the use of nicotine replacement therapy (NRT) may increase cessation rates, and, relative to continued smoking, the use of NRT is considered safer than continued smoking. The majority of women who smoke during pregnancy will require support throughout their pregnancy, delivered either by a trained maternity care provider or via referral to a specialized hospital or community quit-smoking service. The 5As (Ask, Advise, Assess, Assist, Arrange) approach is recommended for organizing screening and treatment in maternity care settings. Additionally, supporting smoking cessation in the postpartum period should also be a priority as relapse rates are high. CONCLUSIONS: There have been several recent updates to clinical practice regarding the treatment of tobacco use in pregnancy. It is important for the latest guidance to be put into practice, in all maternity care settings, in order to decrease rates of smoking in pregnancy and improve pregnancy outcomes.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".