A Guide to Opportunities and Challenges of Developing a Virtual Reality Simulation for Disaster Medicine Courses: A Letter to Editor.
Bibliographic record
Abstract
The advancement of technology has significantly impacted the student population, with many young people now spending a large portion of their time engaging with various forms of technology (1).As such, it is imperative for educational systems to adapt and integrate new technologies into their frameworks in order to meet the evolving needs of students (2).Virtual reality (VR) technology has gained substantial popularity among students and addressing its integration could be a crucial step towards bridging educational gaps (3).VR technology presents promising opportunities for the training and education of disaster medicine practitioners.VR simulations provide realistic, immersive environments that allow for frequent, cost-effective practice of disaster response skills, in contrast to traditional live drills (4).These simulations can be adapted to various scenarios, including infectious disease outbreaks like Ebola, and can incorporate physiological models to mimic patient conditions and treatment outcomes (5).The applications of VR in disaster medicine span basic education, professional training, and psychotherapy (6).Research has demonstrated that VR simulations can enhance knowledge acquisition, boost confidence, and realistically simulate clinical environments for different disaster scenarios (4).While VR technology shows potential as a competitive, cost-effective supplement to existing training approaches, further development is needed to cover a wider range of disaster scenarios in hospital settings (4).However, this poses the question: does the current educational system possess the capacity to embrace this expansive platform?Extensive investigations have revealed that for an
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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.010 | 0.011 |
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".