What's been trending with OSCEs in pharmacy education over the last 20 years? A bibliometric review and content analysis
Bibliographic record
Abstract
BACKGROUND: Objective structured clinical examinations (OSCEs) remain an integral part of pharmacy education. This study aimed to characterize key researchers, areas, and themes in pharmacy education OSCEs using a bibliometric review with content analysis. METHODS: A bibliometric review was conducted on literature from over 23 years from January 2000 to May 2023. Articles focusing on any type of OSCE research in pharmacy education in both undergraduate and postgraduate sectors were included. Articles were excluded if they were not original articles or not published in English. A summative content analysis was also conducted to identify key topics. RESULTS: A total of 192 articles were included in the analysis. There were 242 institutions that contributed to the OSCE literature in pharmacy education, with the leading country being Canada. Most OSCE research came from developed countries and were descriptive studies based on single institution data. The top themes emerging from content analysis were student perceptions on OSCE station styles (n = 98), staff perception (n = 19), grade assessment of OSCEs (n = 145), interprofessional education (n = 11), standardized patients (n = 12), and rubric development and standard setting (n = 8). IMPLICATIONS: There has been a growth in virtual OSCEs, interprofessional OSCEs, and artificial intelligence OSCEs. Communication rubrics and minimizing assessor variability are still trending research areas. There is scope to conduct more research on evaluating specific types of OSCEs, when best to hold an OSCE, and comparing OSCEs to other assessments.
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 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 | 0.056 | 0.186 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.166 | 0.195 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 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".