Virtual reality simulation for facilitating critical reflection and transformative learning: pedagogical, practical, and ethical considerations
Bibliographic record
Abstract
There is growing recognition that preparing health professionals to work with complex social issues in the delivery of healthcare requires distinct theoretical and pedagogical approaches. Recent literature highlights the significance of employing simulated environments which aim to immerse learners in the experiences of diverse populations and bridge the gap between academic learning and lived realities across a diverse society. Virtual Reality (VR) is gaining traction as a promising pedagogical approach in this context. VR has been argued to offer distinct advantages over traditional educational methods by allowing learners to see the world through the eyes of diverse populations, and to learn about social injustices while immersed in a mediated environment. It also has practical benefits in its capacity to expose large number of students to these topics with relatively modest resources compared to other approaches. This debate article explores VR as an innovative pedagogical approach for facilitating critical reflection, dialogue and transformative learning about social issues in health professions education (HPE). It examines the potential affordances as well as risks and dangers of integrating VR into educational programs and highlights key pedagogical, practical, and ethical considerations. Emphasis is placed on the importance of these considerations in efforts toward ethical, safe, and respectful use of VR in educational settings. This paper contributes to the ongoing dialogue on VR simulation as an innovative approach to HPE and highlights the importance of creating conditions that maximize its educational benefits and minimize potential harms.
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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 | 0.039 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.006 | 0.004 |
| 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".