Expanding Interdisciplinarity: A Bibliometric Study of Medical Education Using the Medical Education Journal List-24 (MEJ-24)
Bibliographic record
Abstract
Introduction: Interdisciplinary research, which integrates input (e.g., data, techniques, theories) from two or more disciplines, is critical for solving wicked problems. Medical education research is assumed to be interdisciplinary. However, researchers have questioned this assumption. The present study, a conceptual replication, clarifies the nature of medical education interdisciplinarity by analyzing the citations of medical education journal articles. Method: The authors retrieved the cited references of all articles in 22 medical education journals between 2001-2020 from Web of Science (WoS). We then identified the WoS classifications for the journals of each cited reference. Results: We analyzed 31,283 articles referencing 723,683 publications. We identified 493,973 (68.3%) of those cited references in 6,618 journals representing 242 categories, which represents 94% of all WoS categories. Close to half of all citations were categorized as "education, scientific disciplines" and "healthcare sciences and services". Over the study period, the number of references consistently increased as did the representation of categories to include a diversity of topics such as business, management, and linguistics. Discussion: Our study aligns with previous research, suggesting that medical education research could be described as inwardly focused. However, the observed growth of categories and their increasing diversity over time indicates that medical education displays increasing interdisciplinarity. Now visible, the field can raise awareness of and promote interdisciplinarity, if desired, by seeking and highlighting opportunities for future growth.
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 | 0.011 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.120 | 0.144 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| 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".