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Record W4409671632 · doi:10.1080/09581596.2025.2493790

Exploring the pharmacist’s role in pregnancy care: perceptions, practices, competencies, and barriers

2025· article· en· W4409671632 on OpenAlexaff
Anan S. Jarab, Walid Al‐Qerem, Karem H. Alzoubi, Yukta Sughand, Shrouq Abu Heshmeh, Yazid N. Al Hamarneh, Judith Eberhardt

Bibliographic record

VenueCritical Public Health · 2025
Typearticle
Languageen
FieldMedicine
TopicPregnancy and Medication Impact
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPharmacistPerceptionNursingPregnancyMedicinePsychologyPharmacyFamily medicine

Abstract

fetched live from OpenAlex

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.

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)0.001
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.805
Threshold uncertainty score0.921

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.008
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 integrity0.0000.001
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 head0.176
GPT teacher head0.427
Teacher spread0.251 · 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 designOther design
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

Citations1
Published2025
Admission routes1
Has abstractyes

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