Creating a Successful Virtual Reality–Based Medical Simulation Environment: Tutorial
Bibliographic record
Abstract
Innovation in medical education is not only inevitable but a requirement. Manikin-based simulation is currently the gold standard for supplemental clinical training; however, this modality requires significant equipment and personnel to operate. Virtual reality (VR) is emerging as a new method of delivering medical simulation sessions that requires less infrastructure but also allows for greater accessibility and flexibility. VR has slowly been integrated into the medical curriculum in some hospitals; however, more widespread adoption would transform the delivery of medical education for future clinicians. This tutorial introduces educators to the BUILD REALITY (begin, use, identify, leverage, define, recreate, educate, adapt, look, identify, test, amplify) framework, a series of practical tips for designing and implementing a VR-based medical simulation environment in their curriculum. The suggestions are based on the relevant literature and the authors' personal experience in creating and implementing VR environments for medical trainees. Altogether, this paper provides guidance on conducting a needs assessment, setting objectives, designing a VR environment, and incorporating the session into the broader medical curriculum.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".