How Augmenting Reality Changes the Reality of Simulation: Ethnographic Analysis
Bibliographic record
Abstract
BACKGROUND: Simulation-based medical education (SBME) provides key medical training for providers to safely and ethically practice high-risk events. Augmented reality (AR)-enhanced simulation projects digital images of realistic examination findings into a participant's field of view, which allows nuanced physical examination findings such as respiratory distress and skin perfusion to be prominently displayed. It is unknown how AR compares to traditional mannequin (TM)-based simulation with regard to influencing participant attention and behavior. OBJECTIVE: The purpose of this study is to use video-based focused ethnography-a problem-focused, context-specific descriptive form of research whereby the research group collectively analyzes and interprets a subject of interest-to compare and categorize provider attention and behavior during TM and AR and provide suggestions for educators looking to delineate these 2 modalities. METHODS: Twenty recorded interprofessional simulations (10 TM, 10 AR) featuring a decompensating child were evaluated through video-based focused ethnography. A generative question was posed: "How do the attention and behavior of participants vary based on the simulation modality?" Iterative data collection, analysis, and pattern explanation were performed by a review team spanning critical care, simulation, and qualitative expertise. RESULTS: The attention and behavior of providers during TM and AR simulation clustered into three core themes: (1) focus and attention, (2) suspension of disbelief, and (3) communication. Participants focused on the mannequin during AR, especially when presented with changing physical examination findings, whereas in TM, participants focused disproportionately on the cardiorespiratory monitor. When participants could not trust what they were seeing or feeling in either modality, the illusion of realism was lost. In AR, this manifested as being unable to physically touch a digital mannequin, and in TM, participants were often unsure if they could trust their physical examination findings. Finally, communication differed, with calmer and clearer communication during TM, while AR communication was more chaotic. CONCLUSIONS: The primary differences clustered around focus and attention, suspension of disbelief, and communication. Our findings provide an alternative methodology to categorize simulation, shifting focus from simulation modality and fidelity to participant behavior and experience. This alternative categorization suggests that TM simulation may be superior for practical skill acquisition and the introduction of communication strategies for novice learners. Meanwhile, AR simulation offers the opportunity for advanced training in clinical assessment. Further, AR could be a more appropriate platform for assessing communication and leadership by more experienced clinicians due to the generated environment being more representative of decompensation events. Further research will explore the attention and behavior of providers in virtual reality-based simulations and real-life resuscitations. Ultimately, these profiles will inform the development of an evidence-based guide for educators looking to optimize simulation-based medical education by pairing learning objectives with the ideal simulation modality.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".