Explorando el metaverso en la educación de estudiantes de salud: revisión de alcance
Bibliographic record
Abstract
Objetivo: mapear la literatura respecto de incorporar el metaverso a la educación de estudiantes de carreras del área de salud. Método: revisión de alcance, acorde recomendaciones del JBI y Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR), realizada en Web of Science , Medical Literature Analysis and Retrieval System Online (MEDLINE) vía PubMed, Embase, Scopus, Cumulative Index to Nursing and Allied Health (CINAHL), Latin American and Caribbean Health Sciences Literature (LILACS) y ProQuest. Resultados: fueron incluidos 23 registros, publicados entre 2020 y 2023, desarrollados en 10 países. El metaverso destaca por permitir simular casos hipotéticos, haciendo que la educación resulte interactiva y atractiva. Sin embargo, enfrenta limitaciones, incluyendo la posible despersonalización de los estudiantes, preocupación por la seguridad y privacidad de los datos, además del elevado costo de implementación y mantenimiento de su infraestructura. Conclusión: el metaverso posibilita el desarrollo de competencias clínicas que facilitan la construcción de identidad profesional del estudiante. No obstante, puede no ser equitativo, considerando que demanda recursos y educadores con conocimientos para implementarlo, contribuyendo a acentuar desigualdades en la educación de estudiantes.
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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 | 0.264 | 0.515 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.012 |
| Bibliometrics | 0.025 | 0.016 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| 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".