Simulation-based training in medicine: a scientometric investigation of Scopus-indexed articles
Bibliographic record
Abstract
ABSTRACT Introduction: Simulated training (ST) in healthcare is a method that exposes students to complex clinical scenarios in controlled environments, allowing them to practice and develop skills without risk to the patient. However, few studies have analyzed the scenario of ST in medical education in the university environment for the development of medical skills. Objective: The study analyzed the use of ST in universities in the field of medical education. Method: We identified relevant articles on simulated training and medical education using the main search terms. The Scopus database was used. VOSviewer was used to carry out the bibliometric analysis. Results: The analysis included 3,968 articles. There has been an increase in publications on ST in medical education. Most of the publications came from developed countries, especially the United States, the United Kingdom and Canada. BMC Medical Education was the journal that published the most articles on the subject. The main research hotspots identified were clinical competence, curriculum and computer simulation. Conclusion: Simulated training in medical education has attracted the attention of researchers over the years, with an increase in scientific production in the area. Bibliometric analysis suggests that this area will continue to grow, with an emphasis on clinical competence, interns and residencies, curriculum, computer simulation, surgical training, resuscitation and artificial intelligence.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.009 | 0.023 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".