Improving Emergency Training for Earthquakes through Immersive Virtual Environments and Anxiety Tests: A Case Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Because of the occurrence of severe and large magnitude earthquakes each year, earthquake-prone countries suffer considerable financial damages and loss of life. Teaching essential safety measures will lead to a generation that can perform basic procedures during an earthquake, which is an essential and effective solution in preventing the loss of life in this natural disaster. In recent years, Virtual Reality (VR) technology has been a tool used to educate people on safety matters. This paper evaluates the effect of education and premonition on the incorrect decision-making of residents under the stressful conditions of an earthquake. For this purpose, a virtual model has been designed and modeled based on a proposed classroom in a school in the city of Tehran to simulate a virtual learning experience. In contrast, the classroom represents a realistic method of learning. Accordingly, each educational scenario, presented in reality and the virtual model, respectively, was conducted on a statistical sample of 20 students within the range of 20 to 25 years of age. Among the mentioned sample, the first group of 10 students was taught safety measures in a physical classroom. The second group of 10 students participated in a virtual classroom. Evaluation tests on safety measures against earthquakes were distributed after two weeks. Two self-reporting tests of Depression, Anxiety, Stress Scale (DASS) and Beck Anxiety Inventory (BAI) tests were assigned to the second group to evaluate the effect of foresight under two different scenarios. The results indicate that teaching through VR technology yields a higher performance level than the in-person education approach. Additionally, the ability to detect earthquakes ahead is an influential factor in controlling anxiety and determining the right decisions should the event occur.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it