Deconstructing Participant Behaviors in Virtual Reality Simulation: Ethnographic Analysis
Bibliographic record
Abstract
Background: Virtual reality (VR)-based simulation is an increasingly popular tool for simulation-based medical education, immersing participants in a realistic, 3D world where health care professionals can observe nuanced examination findings, such as subtle indicators of respiratory distress and skin perfusion. However, it remains unknown how the VR environment affects participant behavior and attention. Objective: This study aimed to describe clinician attention and decision-making behaviors during interprofessional pediatric resuscitation simulations performed in VR. We used video-based focused ethnography to describe how participant attention and behavior are altered in the VR environment and reflect how these changes may affect the educational profile of VR simulation. Methods: The research team analyzed scenarios with the question, "How does a completely virtual reality environment alter participant attention and behavior, and how might these changes impact educational goals?" Video-based focused ethnography consisting of data collection, analysis, and pattern explanation was conducted by experts in critical care, resuscitation, simulation, and medical education until data saturation was achieved. Results: Fifteen interprofessional VR simulation sessions featuring the same scenario-a child with pneumonia and sepsis-were evaluated. Three major themes emerged: Source of Truth, Cognitive Focus, and Fidelity Breakers. Source of Truth explores how participants gather and synthesize information in a VR environment. Participants used the patient's physical examination over ancillary data sources, such as the cardiorespiratory monitor, returning to the monitor when the examination did not align with expectations. Cognitive Focus describes the interplay between thinking, communicating, and doing during a VR simulation. The VR setting imposed unique cognitive demands, requiring participants to process information from multiple sources, make rapid decisions, and execute tasks during the scenario. Participants experienced increased task burden when virtual tasks did not mirror real-world procedures, leading to delays and fixation on certain actions. Fidelity Breakers reflects how technical and environmental factors disrupted focus and hindered learning. Navigational challenges, such as unintended teleportation and difficulties interacting with the virtual patient and equipment, disrupted participant immersion. These challenges underscore the current limitations of VR in reproducing the tactile and procedural aspects of real clinical care. Conclusions: Participants' focus on the physical examination findings in VR, as opposed to the cardiorespiratory monitor, potentially indicates simulation of an identical, more patient examination-centered approach to clinical data gathering. In addition, the multiple data sources allowed for participant cognitive load and task burden that may better mirror real-life clinical care. However, technical features that required straying from real-world task completion, as well as other navigational and interactional challenges in VR, led to breaks in fidelity and shifted focus away from the learning objectives. These findings underscore the need for continued research on how simulation modality, fidelity, and technical challenges may influence participant attention and behavior, to allow thoughtful alignment between desired learning objectives and mode of training.
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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 | 0.022 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".