Exploring the metaverse in the education of healthcare students: A scoping review
Bibliographic record
Abstract
OBJECTIVE: to map the literature on the incorporation of the metaverse in the education of undergraduate healthcare students. METHOD: scoping review following the recommendations of the JBI and Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR), performed on Web of Science, Medical Literature Analysis and Retrieval System Online (MEDLINE) via PubMed, Embase, Scopus, Cumulative Index to Nursing and Allied Health (CINAHL), Latin American and Caribbean Health Sciences Literature (LILACS) and ProQuest. RESULTS: a total of 23 records were included, published between 2020 and 2023, and developed in 10 countries. The metaverse allows the simulation of hypothetical cases, making education interactive and attractive. However, it faces limitations, including the possibility of depersonalizing students, concerns about data security and privacy, and the high cost of implementing and maintaining its infrastructure. CONCLUSION: the metaverse enables the development of clinical competencies that support the construction of students' professional identity. However, it may not be equitable, as it requires resources and knowledge from educators to implement it, contributing to increasing inequality in the education of healthcare students. BACKGROUND: (1) The metaverse is promising in the education of undergraduate healthcare students. BACKGROUND: (2) The metaverse makes education interactive and attractive. BACKGROUND: (3) It promotes the protagonism of students in the teaching-learning process. BACKGROUND: (4) It presents risks related to data security and privacy. BACKGROUND: (5) High cost of implementing and maintaining its infrastructure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".