Explorando o metaverso na educação de estudantes da saúde: revisão de escopo
Bibliographic record
Abstract
Objetivo: mapear a literatura a respeito da incorporação do metaverso na educação de estudantes de graduação na área da saúde. Método: revisão de escopo de acordo com as recomendações do JBI e Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR), conduzida na Web of Science , Medical Literature Analysis and Retrieval System Online (MEDLINE) via PubMed, Embase, Scopus, Cumulative Index to Nursing and Allied Health (CINAHL), Literatura Latino-Americana e do Caribe em Ciências da Saúde (LILACS) e ProQuest. Resultados: foram incluídos 23 registros publicados de 2020 a 2023 e desenvolvidos em 10 países. O metaverso destaca-se por permitir a simulação de casos hipotéticos, tornando a educação interativa e atrativa. Apesar disso, enfrenta limitações, incluindo possibilidade de despersonalização dos estudantes, preocupações com segurança de dados e privacidade, além do custo elevado de implementação e manutenção de sua infraestrutura. Conclusão: o metaverso viabiliza o desenvolvimento de competências clínicas que subsidiam a construção da identidade profissional do estudante. Todavia, pode não ser equitativo, na medida em que demanda recursos e educadores com domínio para implementá-lo, contribuindo para acentuar a desigualdade na educação de estudantes.
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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 | 0.129 | 0.394 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.012 |
| Bibliometrics | 0.031 | 0.027 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".