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.
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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 | 0.024 | 0.146 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.174 | 0.240 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".