Metaverse technologies in acute care medical education: A scoping review
Bibliographic record
Abstract
Background: The concept of the metaverse is a virtual world that immerses users, allowing them to interact with the digital environment. Due to metaverse's utility in collaborative and immersive simulation, it can be advantageous for medical education in high-stakes care settings such as emergency, critical, and acute care. Consequently, there has been a growth in educational metaverse use, which has yet to be characterized alongside other simulation modalities literature. This scoping review aims to provide a comprehensive overview of all research describing metaverse use in education for emergency, critical, and acute care. Methods: We used Arksey and O'Malley's framework with the Levac et al. modifications to conduct a scoping review by searching these five databases (MEDLINE, EMBASE, ERIC, Web of Science, and Education Source). The framework comprises six steps: (1) identifying the research question; (2) identifying relevant literature; (3) study selection; (4) data extraction; (5) collating, summarizing, and reporting data; and (6) consultation with key informants. Relevant themes and trends were extracted and mapped for reporting. Results: The search yielded 8175 citations, which ultimately led to data extraction from 65 articles. Studies evaluated metaverse programs for the learning and assessment of both technical skills (management of code blue, sepsis, stroke, etc.) and nontechnical skills (e.g., interprofessional collaboration, communication, critical decision making). Barriers to metaverse implementation include technical challenges and difficulty evaluating educational effectiveness. Conclusions: The results of this scoping review highlight the current applications of metaverse as an educational tool, its identified strengths and weaknesses, and further comparison between metaverse and other educational modalities such as high-fidelity simulation. This work provides direction for future primary and secondary research that can aid educational programmers and curriculum planners in maximizing metaverse potential in emergency, critical, and acute medical education.
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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 | 0.020 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.029 | 0.021 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".