The use, perceptions and knowledge of safety of over-the-counter medications during pregnancy in a Canadian population
Bibliographic record
Abstract
BACKGROUND: The prevalence of prenatal over-the-counter medication use in Canadian women is unknown. METHODS: A cross-sectional study of prenatal over-the-counter medication use and safety knowledge was conducted among pregnant and post-partum women attending an academic hospital obstetrics clinic. RESULTS: Seventy-two women participated; 90.3% were Caucasian, 69.4% had a college/university degree, and 61.1% lived in an urban area. Of the 72 women, 87.5% used over-the-counter medications prenatally, first (55.6%), second (65.3%), and third (47.2%) trimesters, with prenatal acetaminophen use most common (72.2%). Women who used over-the-counter medications 1-0onths before conception were more likely to use over-the-counter medications during pregnancy, and 18% of women initiated over-the-counter medications in pregnancy. Women self-reported a medium level of over-the-counter medication safety knowledge (73.6%) and responded that not all over-the-counter medications are safe during pregnancy (95.8%). CONCLUSION: Despite limited safety profiles of some over-the-counter medications, pre-conception and prenatal over-the-counter medication use was high. Further research on the risk of over-the-counter medications and combinations in pregnancy is needed to help women to make safe choices during pregnancy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".