Vaping guidance and women's decision-making during pregnancy & postpartum
Bibliographic record
Abstract
Background: Vaping during pregnancy remains under researched and under reported, making appropriate prevention, health promotion and intervention difficult to design and mount. In this article we assessed the experiences and considerations of women who vape during pregnancy and/or within 2 years post-partum, in order to underpin realistic and informative health information for women and providers in face of conflicting and minimal guidance. Design: 22 interviews were conducted with pregnant and post-partum women who vape(d) nicotine, cannabis, or both during pregnancy and/or after delivery. Methods: Participants who were pregnant or postpartum were recruited via social media and interviews conducted on Zoom or by telephone, recorded and transcribed. Data were coded in NVivo 12 and analyzed using a combined deductive and inductive approach, and principles of abductive analysis were applied to the data. Results: Three overarching themes related to decision making about vaping are described: women's agency in information seeking, approaches to assessing information, and ambivalence regarding vaping practices. Women looked for information on the health effects of vaping during pregnancy and made differing decisions in the context of limited research and guidance. At times, family, friends, partners, and internet resources influenced their decisions. Some women dealt with ambivalence by vaping only in private, while alone, and at home or as a convenience. The women were uniformly aware of societal judgement regarding pregnancy and substance use in general and feared being addressed by friends or strangers about vaping. Conclusion: In the absence of definitive research and unambiguous clinical guidance, the women felt limited in finding accurate advice, but demonstrated agency in information seeking and assessment. Nonetheless, they also recounted their ambivalence regarding their vaping decisions and practices. We created varied knowledge information products to fill this void.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".