Feasibility of Augmented Reality in Clinical Simulations: Using Google Glass With Manikins
Bibliographic record
Abstract
BACKGROUND: Studies show that students who use fidelity-based simulation technology perform better and have higher retention rates than peers who learn in traditional paper-based training. Augmented reality is increasingly being used as a teaching and learning tool in a continual effort to make simulations more realistic for students. OBJECTIVE: The aim of this project was to assess the feasibility and acceptability of using augmented reality via Google Glass during clinical simulation scenarios for training health science students. METHODS: Students performed a clinical simulation while watching a video through Google Glass of a patient actor simulating respiratory distress. Following participation in the scenarios students completed two surveys and were questioned if they would recommend continued use of this technology in clinical simulation experiences. RESULTS: We were able to have students watch a video in their field of vision of a patient who mimicked the simulated manikin. Students were overall positive about the implications for being able to view a patient during the simulations, and most students recommended using the technology in the future. Overall, students reported perceived realism with augmented reality using Google Glass. However, there were technical and usability challenges with the device. CONCLUSIONS: As newer portable and consumer-focused technologies become available, augmented reality is increasingly being used as a teaching and learning tool to make clinical simulations more realistic for health science students. We found Google Glass feasible and acceptable as a tool for augmented reality in clinical simulations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".