Knowledge, Information Sources, and Institutional Trust of Patients Regarding Medication Use in Pregnancy: A Systematic Review
Bibliographic record
Abstract
Objective: The objective of our study is to characterize the knowledge, information sources, and institutional trust of patients regarding medication use in pregnancy. Materials and methods: We conducted a review of three databases: MEDLINE, EMBASE, and CINAHL. We included observational studies and knowledge assessments that examined the knowledge, attitudes, beliefs or information sources of pregnant patients related to medication use during pregnancy. Extraction was completed by two independent reviewers, outcomes were summarized descriptively, and appraisal was conducted. Results: Of the 1359 search results, 34 studies met inclusion criteria. Thus, our systematic review encompasses the beliefs of 11,757 pregnant participants. In most studies, participants described apprehension regarding potential risks to the fetus and the inadequacy of safety information. Across the 23 knowledge assessments, the majority of studies reported patient misconceptions about prescription medication in pregnancy. The most preferred information source was a healthcare provider. However, many participants expressed frustration, mistrust, and skepticism regarding physician knowledge. A common source of mistrust was due to perceived physician self-interest as well as a lack of education tailored to pregnancy. Consequently, informal sources of information were also popular. Conclusion: There is a need to improve the health literacy and trust among pregnant patients regarding drug prescribing. There are modifiable risk factors for mistrust that require further attention.
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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 | 0.010 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".