Characteristics, trends, and impact of immersive technologies in medical education: A bibliometric analysis
Bibliographic record
Abstract
Introduction: in the realm of medical education, immersive technologies such as Virtual Reality (VR), Augmented Reality (AR), and the Metaverse are provoking a profound and fast shift. These technologies are fostering the development of essential professional competencies in healthcare. Nevertheless, conducting a systematic evaluation of the scientific output in this area and its impact on the learning process of health professionals is critical. Objective: to analyze the scientific production related to these technologies in medical education, identifying research trends and their impact on the learning of health professionals. Methods: a bibliometric analysis was conducted using the Scopus database until May 2023. VOSviewer software was employed to analyze the interaction among thesauri. Results: a total of 243 documents with 4600 citations were identified. The output on immersive technologies in medical education is emerging. The United States and Canada are the main producers, and an increase in international collaboration has been observed. The topics of greatest interest to authors were "humans," "virtual reality," and "education." The thematic areas identified were "primary studies designs," "technical skills training," "curricular proposals," and "computer sciences." Conclusions: there is a steady increase in the production and citations of research on immersive technologies, mostly originating from high-income countries. No clear areas of specialization have yet been identified, although studies are focused on integrating these technologies into the curriculum and on learning technical skills
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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 | 0.012 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.207 | 0.232 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".