Two Decades of Medical Education Scholarship: Mapping Collaboration and Thematic Shifts Using Web of Science (2000–2019)
Bibliographic record
Abstract
Introduction: The field of medical education (ME) has grown substantially over the past decades, yet questions remain about its scope and boundaries. This study examines how research topics and institutional collaborations have evolved in ME from 2000 to 2019. Methods: Adopting a post-positivist stance and using bibliometric network analyses, we examined metadata from 31,338 publications across 22 core ME journals indexed in the Web of Science. We analyzed trends in institutional collaboration and the development of research themes. Extracted metadata included authors' institutional affiliations and KeyWords Plus (n = 18,218). Bibliometric analyses were conducted using VOSviewer, a widely used tool for network mapping. We generated co-authorship networks to trace institutional collaboration and co-word networks to identify thematic clusters. Results: Co-authorship networks revealed increasing collaboration, with U.S. institutions remaining central and Canadian and Dutch institutions gaining prominence. Co-word analyses identified three stable clusters-teaching and learning, quantitative, and psychosocial-with teaching and learning dominant across all periods and the quantitative cluster expanding in recent years. Discussion: Findings show the consolidation of teaching and learning as the foundation of ME, alongside diversification through quantitative and psychosocial themes. Growing collaborations suggest the field's maturation, though geographic imbalances persist. Limitations include reliance on a restricted set of Web of Science journals, which overrepresent English-language and highly cited publications, and the use of KeyWords Plus as a proxy for themes. This study offers an evidence-based mapping of ME's evolution and provides a framework for future research on the interdisciplinary and global dynamics of the field.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.044 | 0.062 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".