Integrating Google Glass into simulation-based training: experiences and future directions
Bibliographic record
Abstract
Background: Education experts are starting to explore the potential uses of wearable technology and augmented reality in simulation-based training. In this article, we summarize our experiences with using Google Glass in simulation-based training and discuss potential future directions with this advanced technology. Methods: Emergency medicine residents and medical students participated in a pilot study where each team captain was asked to wear Google Glass during 15 separate simulation-based training sessions. Video obtained from Google Glass was analyzed and utilized during debriefing sessions for the residents and medical students. Results: We were able to successfully integrate Google Glass into simulation-based training and debriefing. During the analysis of each recording, observations were noted about the events that transpired and this data was used to provide instructional feedback to the residents and medical students for self-reflection and appraisal. Post-exercise surveys were conducted after each simulation session and all participants noted that Google Glass did not interfere with their simulation experience. Google Glass enabled the observers to analyze the team captain’s primary visual focus during the entire simulation scenario and feedback was provided based on the data recorded. Conclusions: Wearable technologies such as Google Glass can be successfully integrated into simulation-based training exercises without disrupting the learners’ experience. Data obtained from this integration can be utilized to improve debriefing sessions and self-reflection. Future research is underway and required to evaluate other potential uses for wearable technology in simulation-based 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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".