Shaping the Future of Healthcare: The University of Manitoba's Virtual Reality Interprofessional Education (VR-IPE) Program
Bibliographic record
Abstract
In many health sciences academic programs, interprofessional collaboration among students and faculty is an accreditation requirement. Time, curriculum alignment and physical location continue to be significant barriers to providing experiential learning opportunities, which require innovative means to provide interprofessional education experiences. Virtual Reality (VR) is a growing area of simulation-based learning that removes obstacles like physical co-location and can join interprofessional groups of students to engage in experiential learning activities. Members of the presentation panel met in February 2023 and were overwhelmingly in favour of exploring the development of a metaverse within the Rady Faculty of Health Sciences (Dentistry, Medicine, Nursing, Pharmacy, Rehabilitation Sciences). Funding from a Strategic Initiatives Support Fund allowed us to secure the hardware and software necessary to create a metaverse in our faculty that would allow for inter- and intraprofessional learning activities. This panel will present the phases of implementing a virtual reality program in a large health professions faculty of over 3500 students.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".