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Record W4387033846 · doi:10.1177/17455057231199394

The use, perceptions and knowledge of safety of over-the-counter medications during pregnancy in a Canadian population

2023· article· en· W4387033846 on OpenAlexaffabout
Eoin Casey, Maria P. Vélez, Laura Gaudet, Susan B. Brogly

Bibliographic record

VenueWomen s Health · 2023
Typearticle
Languageen
FieldMedicine
TopicPregnancy and Medication Impact
Canadian institutionsQueen's University
Fundersnot available
KeywordsOver-the-counterMedicinePregnancyFamily medicinePopulationObstetricsCross-sectional studyMedical prescriptionEnvironmental healthNursing

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.141
Threshold uncertainty score0.940

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.028
GPT teacher head0.340
Teacher spread0.312 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2023
Admission routes2
Has abstractyes

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