Drug Labeling and Risk Perceptions of Teratogenicity: A Survey of Pregnant Canadian Women and Their Health Professionals
Bibliographic record
Abstract
There is a general perception that medicinal drugs are not safe in pregnancy despite the fact that fewer than 30 drugs have been shown to cause major malformations in humans. A large number of women need medications in pregnancy to treat pregnancy-induced conditions, acute illnesses, and chronic diseases. The objectives of this study were the following: (1) to characterize the perception of teratogenic risk by pregnant women and their partners and by health professionals and (2) to examine the most reassuring way to present data on a drug for nausea and vomiting of pregnancy that has been proven to be safe to the fetus. A convenience sample of pregnant Canadian women and their partners, pharmacists, nurses, physicians, and hospital workers were asked to choose the "safest" among four drugs by statements describing their safety. Although the text of all four was similar, the title and narrative were modified to be more or less "reassuring" by the use of more or less terms such as malformations and abnormalities. Health professionals rated the teratogenic risk significantly lower than the parents, but even they rated the drugs as not safe, despite a scientifically reassuring text. Sixty percent of the 240 participants, regardless of their perception of teratogenic risk, believed the four drugs were of similar risks. However, in the other 40%, the less "reassuring" text led to higher teratogenic perception, and the more reassuring options tended to decrease the false perception of teratogenic risk. It was concluded that in general, four different versions of reassuring text describing a scientifically proven safe drug in pregnancy did not lead expecting parents to believe they were safe. Among those who did not rank the four drugs as having equal safety/risk, the less "reassuring" text led to a higher perception of teratogenic risk. Even health professionals reading the labels describing safe drugs rated them as unsafe. Presently, the perception of teratogenic risk is strong even for safe drugs and is difficult to change even with evidence-based facts.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".