VR-NRP: A development study of a virtual reality simulation for training in the neonatal resuscitation program
Bibliographic record
Abstract
Objectives: Virtual reality (VR) offers the potential to provide a lifelike, safe, and interactive environment where healthcare providers can practice and refresh their skills. The Neonatal Resuscitation Program (NRP) is an evidence-based and standardized approach for training healthcare providers on the resuscitation of the newborn where VR can be applied. Here we describe a development study for a VR-NRP simulation. This contribution is relevant for researchers and developers in the health sector interested in the integration of VR and other extended reality (XR) technologies in medical education and training. Methods: For the implementation of the VR simulation, we used the Unity game engine, a VR-capable laptop, and an HTC Vive Pro Head-Mounted Display. We focused on the skill of positive pressure ventilation (PPV) using a bag and mask as the main scenario for the simulation since this is a foundational skill in NRP. To validate the prototype, we compared the VR-NRP simulation with 360° immersive VR videos in a crossover study involving 30 health-care providers and students, collecting various data through questionnaires and skill assessments by NRP instructors. Results: We described in detail the creation process by which a highly realistic VR simulation was produced reflecting the visual elements and sounds of a Neonatal Intensive Care Unit in a hospital setting. In the crossover study, we found both VR technologies were positively viewed by healthcare professionals and performed very similarly. However, the VR simulation provided a significantly increased feeling of presence. Participants found the VR simulation more useful and reported higher confidence in NRP skills such as proper mask placement and newborn response evaluation, reflecting improved experiential learning outcomes. Conclusion: This research represents a step forward in understanding how VR technologies can be developed and applied for effective, immersive medical training, increasing the availability of NRP refresher sessions, and providing insights into similar applications.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".