Exploring the pharmacist’s role in pregnancy care: perceptions, practices, competencies, and barriers
Bibliographic record
Abstract
Pregnant women often worry about medication effects on their unborn child, leading to inadequate care and potential risks to maternal and fetal health. Research on community pharmacists’ role in prenatal care is limited. This cross-sectional study used a validated, self-administered survey distributed to 405 pharmacists across the United Arab Emirates to evaluate their perceptions, practices, competencies, and barriers in pregnancy care. Data were analyzed using quantile regression to identify associations between demographic and professional factors. Female pharmacists had more favorable perceptions (coefficient = 1.508) and higher competencies (coefficient = 0.457). Pharmacists without pregnancy-related postgraduate training reported less favorable perceptions (coefficient = −2.201) and lower practice levels (coefficient = −0.852). Higher practice levels, reflecting frequent engagement in key pregnancy care activities, were associated with spending more time with patients (coefficient = 0.204) and having favorable perceptions (coefficient = 0.488). Conversely, lower practice levels, indicating less frequent involvement, were linked to less experience and lack of training. Key barriers included insufficient training and limited knowledge of updated guidelines. The findings reflect wider global challenges and highlight the need for training and guidance to strengthen pharmacists’ contribution to maternal 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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".