Perceptions and reported use of extended reality technology in Royal College-Accredited Canadian Simulation Centres: a national survey of simulation centre directors
Bibliographic record
Abstract
Background: Extended reality technology (XR) in simulation-based medical education is becoming more prevalent. This study examined Canadian simulation centre directors' perceptions toward XR and their self-reported adoption of XR within their centres. Methods: We conducted a national, cross-sectional survey study to examine five kinds of XR: Immersive Virtual Environments, Screen-based Virtual Worlds, Virtual Simulators, Immersive Augmented Reality, and Non-immersive Augmented Reality. An electronic survey with multiple-choice, Likert scales, and open-ended questions were developed to identify the current use, degree of satisfaction, and experienced and foreseen challenges with each XR technology. We used the Checklist for Reporting Results of Internet E-Surveys checklist to describe and justify our survey development. All twenty-three Royal College-accredited Canadian simulation centres were invited based on their Royal College membership to complete the survey. Directors and representatives of seventeen (74%) centres participated. Results: Each XR has been used for research or simulation education by about half of the simulation centres, at minimum. The degree of satisfaction among directors with XR ranged from 30% to 45%. Directors frequently cited logistical and fidelity challenges, along with concerns over maintenance. Cost and lack of evidence, and unclear needs were cited as foreseen challenges with the future implementation of XRs. Conclusions: This survey summarizes the status of XR in Canadian simulation centres. The pattern of use, satisfaction levels, and challenges reported by simulation centre directors varied depending on the types of XR.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".