The evolution of simulation-based medical education research: From traditional to virtual simulations
Bibliographic record
Abstract
Background: Simulation-based medical education (SBME) is a widely used method in medical education. This study aims to analyze publications on SBME in terms of countries, institutions, journals, authors, and keyword co-occurrence, as well as to identify trends in SBME research. Methods: We retrieved the Publications on SBME from the Web of Science Core Collection (WoSCC) database from its inception to January 27, 2024. Microsoft Excel 2019, CiteSpace, and VOSviewer were used to identify the distribution of countries, journals, and authors, as well as to determine the research hotspots. Results: We retrieved a total of 11272 publications from WoSCC. The number of documents published in 2022 was the highest in the last few decades. The USA, the UK, and Canada were three key contributors to this field. The University of Toronto, Stanford University, and Harvard Medical School were the top major institutions with a larger number of publications. Konge, Lars was the most productive author, while McGaghie, William C was the highest cited author. BMC Medical Education has the highest number of publications among journals. The foundational themes of SBME are "Patient simulation," "extending reality," and "surgical skills." Conclusions: SBME has attracted considerable attention in medical education. The research hotspot is gradually shifting from traditional simulations with real people or mannequins to virtual, digitally-based simulations and online education. Further studies will be conducted to elucidate the mechanisms of SBME. The utilization of SBME will be more rationalized.
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 | 0.041 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.030 | 0.048 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".