Women’s Experiences with Nicotine and Cannabis Vaping During Pregnancy and Postpartum
Bibliographic record
Abstract
Background/Objectives: There is limited research on vaping during pregnancy and the postpartum period. Amid the legalization of cannabis in Canada, and evolving patterns of nicotine use, there is a growing need to understand how women experience using nicotine and cannabis vaping during pregnancy and postpartum. This information is essential to inform both women and healthcare providers (HCPs) and to develop resources and best practices for supporting women and healthcare services. Methods: In this descriptive study, a sample of 111 women who vaped nicotine and/or cannabis during pregnancy/postpartum was recruited via social media to answer survey questions on reasons for vaping, perceptions of the risks to fetal and maternal health, attitudes toward vaping, and reasons for consulting HCPs regarding vaping during pregnancy. Results: Among the 111 women, 51.4% vaped nicotine, 27.9% vaped cannabis, and 20.7% vaped both. Of the respondents, 63.1% were currently pregnant, while 36.9% were postpartum. Most participants (64.9%) reported vaping daily, followed by 15.3% with an inconsistent pattern, 9.9% vaping 1–2 days a week, and 9% vaping 5–6 days a week. Flavor preferences were prevalent, with fruit flavors being the most popular, followed by menthol/mint and candy, dessert, or sweet flavors. The primary reasons for vaping were relaxation, managing anxiety/depression, enjoyment, and the belief that vaping is less harmful than smoking. Women commonly consulted HCPs about potential harm to their pregnancy, fetal health, and their child’s health. Conclusions: The findings suggest that vaping among pregnant and postpartum women, particularly cannabis vaping, is perceived as healthier than smoking and is often used to manage mental and physical symptoms. These findings were used to create knowledge products to help guide HCPs’ conversations with women and provide evidence-based information on vaping.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".