Immersive Virtual Reality Simulation for Medical Student Procedural Training: Assessment of Cognitive Load and Usability
Bibliographic record
Abstract
Objective In a previous randomized controlled trial, we found immersive virtual reality (VR) simulation to be effective for teaching procedural skills to medical students. We further investigated this interface’s usability and cognitive load. Methods This was a secondary analysis of data from a previous randomized controlled trial. Twenty-two medical students with no or limited experience with VR and chest tube insertion received training for chest tube insertion using a commercially available immersive VR simulation. Participants completed post-training surveys on usability (System Usability Scale, SUS, from 0-100) and cognitive load (Leppink’s scale, 11-point, 10 items). Three types of cognitive loads (intrinsic, extraneous, and germane) were evaluated. Modified Objective Structured Assessment of Technical Skills (OSATS, 5-point, 11 items) for technical skills in a mannequin simulation were assessed after VR training, and in knowledge scores before and after training were extracted to analyze their relationships with usability and cognitive load. Data are presented as median (interquartile range). Results Median scores (%) for the knowledge test were 46.7 (40.0-53.3) at baseline and 86.7 (80.0-90.3) after training. The OSATS score was 40.5 (35.5-49.3), and SUS was 82.5 (73.8-88.8, with significant correlation between these variables (r = 0.51, P = 0.04). The intrinsic, extrinsic, and germane cognitive loads were 3.7 (1.8-6.1), 0.15 (0-1.4), and 9.2 (6.0-10), respectively. Conclusion Cognitive load and usability of immersive VR simulation were reported to be excellent. Along with its effectiveness shown previously, VR simulation is a highly acceptable approach for teaching technical skills to medical students.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".