Virtual Reality for Pediatric Trauma Education - A Face and Content Validation Study
Bibliographic record
Abstract
Purpose: Pediatric trauma is a leading cause of death and disability among children. While trauma education can improve these outcomes, it remains expensive and available only to a few providers worldwide. Innovative educational technologies like virtual reality (VR) can be key to democratizing trauma education. This study, therefore, evaluates the face and content validity of a VR platform designed to enhance pediatric trauma skills. Specifically, we seek to determine whether the platform effectively presents an injured child and comprehensively covers the essential tasks to successfully treat them within a trauma team. Methods: Physicians were invited to test a VR platform simulating a child with blunt head and truncal trauma. After the simulation, they filled out surveys assessing the face and content validity of the scenario, including their opinions on the realism, interaction, ease of use, and the educational content of the platform. Additionally, they completed a cybersickness questionnaire. Demographic data were also collected, including age, gender, country of medical education, and previous experience with VR. A descriptive analysis was performed. Results: Eleven physicians graduated from eight different countries tested the VR platform. Most (87%) found it valuable, and 81% preferred using it over high-fidelity mannequins for training purposes. The platform received more favorable evaluations for non-technical skills training (median: 5, IQR: 5.0 to 5.0) than for technical skills (median: 4, IQR: 3.0 to 5.0). Regarding cybersickness, 73% of the participants reported experiencing any or minimal discomfort during the simulation, and none needed to stop the test due to discomfort. Conclusion: Our initial validation of a VR platform designed for pediatric trauma education was positive. Participants endorsed VR and its potential to enhance performance, particularly in non-technical skills. Encouraged by these results, we will proceed with feasibility and implementation studies, comparing VR to high-fidelity mannequins.
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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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".