Impact of pharmacist-led interventions on pregnancy-related health outcomes: a systematic review
Bibliographic record
Abstract
Pharmacist-led interventions are crucial for improving healthcare outcomes, particularly for pregnant women who face unique medication-related risks. Effective medication management during pregnancy directly impacts maternal and neonatal health, reducing medication errors and adverse drug reactions due to physiological changes and the need to avoid teratogenic substances. This review aimed to systematically evaluate the impact of pharmacist-led interventions on medication adherence, clinical outcomes, and maternal or neonatal health among pregnant women. Following PRISMA guidelines, a comprehensive search of PubMed, Embase, and Scopus was conducted to identify studies from 2013 to 2023 on pharmacist-led interventions in pregnant women. Randomized controlled trials, quasi-experimental studies, and observational studies were included. Data were extracted using a standardized form, and study quality was assessed with Cochrane ROB 2, ROBINS-I, and modified Newcastle-Ottawa Scale. Seven studies from Canada, Australia, Indonesia, Norway, Nigeria, and China were included. Pharmacist interventions, such as medication therapy management, patient education, and the identification of drug interactions, significantly improved medication adherence, clinical outcomes, and patient satisfaction. These interventions were particularly effective in managing hypertensive disorders, asthma, and gestational diabetes mellitus, leading to better health outcomes and fewer adverse events. Pharmacist-led interventions significantly improve health outcomes for pregnant women by enhancing medication adherence and clinical care. Further research is needed to confirm these benefits, standardize outcomes, and explore additional areas of impact to optimize maternal and neonatal health.
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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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".