Quels apports le métavers pourrait-il amener aux systèmes de santé ?
Bibliographic record
Abstract
Health systems are faced with major challenges that put them under pressure to meet users' expectations. These include the increasing complexity of access to qualified human resources and the limitation of financial resources that can be invested. As such, the metaverse could offer solutions to these challenges. In this commentary, we discuss the possible fields of application and contributions of the metaverse to health systems. The identified fields of application include training to strengthen the skills of health professionals, the provision of care to improve the quality of services, communication to facilitate information sharing between different stakeholders, and research to optimize participants' adherence and safety. However, the metaverse can also have negative effects on the people who use it, as well as those who benefit from it. This may include cybercrime, and harm to physical, mental, and social health. Evaluative studies are thus necessary to measure the extent and conditions of its applicability.
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 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 | 0.031 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.016 | 0.029 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".