Pharmacogenomics Education and Knowledge Assessment in Healthcare Curricula: A Scoping Review of the Middle East and North Africa Region
Bibliographic record
Abstract
Pharmacogenomics (PGx) is a crucial part of precision medicine; however, its integration into clinical practice has been slow, primarily due to knowledge gaps regarding pharmacogenomics among healthcare professionals (HCPs). Pharmacogenomics education is considered the most influential barrier to the successful implementation of PGx. In the Middle East and North Africa (MENA) region, several studies have highlighted the lack of PGx education among students and HCPs. This scoping review aims to provide an overview of the existing literature on how PGx is delivered in the curricula and its impact on knowledge acquisition among students and HCPs in the MENA region. A database search of PubMed, Embase, and CINAHL was conducted up to June 2023. Outcomes included PGx education availability, curriculum placement, mode of delivery, and knowledge level. Seven cross-sectional studies were identified (2014-2023), with 57% from the Gulf region. PGx education is mostly taught to undergraduates (71%) through didactic lectures (100%), with only a few studies reporting a standalone course (29%). Participants demonstrated low (43%) to moderate (43%) knowledge levels, assessed using objective scales or subjective self-assessment after completion of the course. Reported barriers to implementing PGx education included knowledge gaps, economic constraints, and system sustainability. PGx education in the MENA region is delivered mainly as an integrated course in professional programs including pharmacy and medicine, and the overall knowledge level was low to moderate. Future research could focus on providing more detailed reporting of the PGx educational landscape, developing standardized assessment tools, and evaluating actual knowledge acquisition among different professions.
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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 | 0.015 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".