Global bibliometric analysis of pharmacists’ competency exam and educational trends
Bibliographic record
Abstract
In 2020, data from 194 WHO countries showed 51 million health workers, including 3.7 million pharmacists, emphasizing competency-based education (CBE) and demanding competency exams in each country. Therefore, this research aimed to map global trends, international relationships, and differences in pharmacists’ competency exam methods using bibliometric analysis. A bibliometric analysis of 597 articles published between 2014 and 2024 in Scopus and Web of Science was conducted using RStudio and Bibliometric, including performance analysis, science mapping, as well as network analysis to uncover publication trends, research clusters, and knowledge differences. Findings showed that the United States led in publication volume (330 articles), followed by Canada and Saudi Arabia. International partnerships were present in 13.57% of publications, with Saudi Arabia showing a particularly high cross-country relationship rate. During this research, dominant themes included pharmacy education, structured clinical exams, and interprofessional relationships. The American Journal of Pharmaceutical Education was the most prolific source, reflecting a focus on advancing competency-based learning. This research identified disparities in pharmacists’ competency exam methods and showed the need for standardized global frameworks associated with local contexts. Innovations such as AI-driven assessments and interprofessional education offered promising solutions for improving competency evaluation. In addition, recommendations included improving the relationship between academic institutions and regulatory bodies, incorporating advanced technologies, as well as associating exams with healthcare needs to improve the quality of pharmacists’ education. By addressing knowledge differences and promoting innovative assessment strategies, this research contributed to global efforts in strengthening pharmacists’ competencies and advancing equitable and high-quality education according to the Sustainable Development Goals (SDGs).
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.019 | 0.030 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".