Design of a Virtual Reality Interactive Training System for Public Health Emergency Preparedness for Major Emerging Infectious Diseases: Theory and Framework
Bibliographic record
Abstract
BACKGROUND: Sufficient public health emergency preparedness (PHEP) is the key factor in effectively responding to and recovering from major emerging infectious diseases (MEIDs). However, in the face of MEIDs, PHEP is insufficient, so it is necessary to improve PHEP. The rapid development of virtual reality and human-computer interaction provides unprecedented opportunities for innovative educational methods. OBJECTIVE: This study designed a virtual reality interactive training system (VRITS) to provide an effective path for improving PHEP in the context of MEIDs so that the public can effectively respond to and recover from MEIDs. METHODS: This study used interactive narrative, situated learning and human-computer interaction theories as a theoretical framework to guide the design of the system. We used the literature research method and the Delphi method; consulted multidisciplinary experts, such as infectious diseases, disease control, psychology, and public health personnel, to determine the educational content framework; and set up an interdisciplinary team to construct an operating system framework for the VRITS. RESULTS: We named the VRITS "People's War Against Pandemic." The educational content framework includes 20 knowledge, emotion, and behavior skills in 5 aspects (cooperating with prevention and control work, improving emergency response ability, guaranteeing supplies and equipment, preparing economic resources, and maintaining physical and mental health). The operating system framework includes virtual interactive training, knowledge corner, intelligent evaluation, and community forum modules, and the core module is the virtual interactive training module. In this module, users control virtual characters to move in various scenes, and then identify and analyze the controllability and harmfulness of the evolving pandemic and select the correct prevention and control strategy to avoid infecting themselves and others. CONCLUSIONS: The development and sharing of the multidisciplinary theoretical framework adopted by People's War Against Pandemic can help us clarify the design ideas and assumptions of the VRITS; predict training results; understand the ability of training to change emergency knowledge, emergency emotion, and behavioral responses to MEIDs; and promote the development of more effective training systems based on virtual reality.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".