Global Insights and Key Trends in Objective Structured Clinical Examination Research Related to Residency Training: A Bibliometric Analysis
Bibliographic record
Abstract
This study presents a comprehensive bibliometric analysis to chart global research trends on the OSCE and residency training. Data were sourced from the Web of Science Core Collection database, covering research conducted from January 1999 to June 2024. CiteSpace and VOSviewer were employed to analyze the selected studies, evaluating publication trends, key contributors, and emerging topics through the co-occurrence mapping and network visualization. In total, 211 publications were identified. From 2005 to 2024, there was an increase in publications related to the OSCE and standardized residency training. The United States and Canada emerged as dominant contributor. Institutional collaborations were led by the University of Ottawa, New York University, and the University of Toronto. The most frequent keywords included "OSCE" (71 occurrences), "residents" (56 occurrences), "performance" (51 occurrences), "competence" (35 occurrences), "skills" (35 occurrences), "education" (31 occurrences), "medical education" (29 occurrences), "reliability" (23 occurrences), "medical students" (17 occurrences), "validity" (17 occurrences), and "clinical competence" (17 occurrences). Cluster analysis of the keywords identified nine clusters, mainly covering residency programs in different disciplines, communication skills, core competencies, and the reliability and validity of the OSCE for residency education. Initial studies emphasized terms such as "performance," "competence," "reliability," and "assessing surgical residents," and recent research continues to emphasize the quality of residency training, instructional effectiveness, and development of communication skills. The application of the OSCE in standardized residency training research is in its developmental phase, and further cross-regional collaboration is necessary. Future research should focus on improving the competence of residents and developing innovative, practice-oriented educational models that align with the evolving needs of residency training.
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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 | 0.019 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.211 | 0.251 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".