Systematic Review And Meta-Analysis On The Gaps In Pharmacy Education System: A Prisma Compliant Study
Bibliographic record
Abstract
Background:Pharmacy education is crucial in shaping the competencies and skills of future pharmacists. However, significant gaps in the current pharmacy education system have been reported globally. These gaps range from outdated curricula and insufficient clinical training to inadequate research opportunities and lack of mental health support for students. Identifying and evaluating these gaps is essential for improving the quality of pharmacy education. Objective:This systematic review and meta-analysis aim to identify and evaluate the gaps in the pharmacy education system worldwide. The study adheres to PRISMA guidelines to ensure transparency and reproducibility. Methods: A comprehensive literature search was conducted using PubMed, Scopus, and Google Scholar databases. Search terms included 'pharmacy education', 'curriculum gaps', 'clinical training in pharmacy', 'pharmacy education system', and 'mental health in pharmacy students'. The inclusion criteria were peer-reviewed articles focused on pharmacy education gaps, studies following PRISMA guidelines, and articles published in English between January 2000 and January 2024. Data were extracted using a standardized form, and the quality of included studies was assessed using the Newcastle-Ottawa Scale. A meta-analysis was performed using random-effects models to account for heterogeneity. Results:Out of 2,500 studies identified, 200 met the inclusion criteria. Key findings include significant gaps in curriculum content, particularly in emerging fields such as personalized medicine, pharmacogenomics, and digital health. Inadequate clinical training and exposure to real-world scenarios were major concerns. Limited research opportunities for undergraduate students and insufficient mental health support systems were also identified. Conclusion:This systematic review and meta-analysis highlight critical areas in pharmacy education that require immediate attention. Updating curricula to include emerging fields, enhancing clinical training, encouraging undergraduate research, and implementing robust mental health support programs are essential steps to address these gaps. The findings and recommendations can guide policymakers, educators, and stakeholders in improving the quality of pharmacy education globally.
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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 | 0.094 | 0.197 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.028 | 0.051 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".