The Effect of Using Virtual Reality on School-Age Children’s and Caregivers’ Anxiety in the Emergency Room: True Experimental Study
Bibliographic record
Abstract
Background: Being treated in an emergency room (ER) could be a stressful experience and trigger anxiety in children. Virtual reality (VR) is a technology-based distraction technique that can be used for school-age children. Objective: In this study, we aimed to identify the effect of using VR in reducing anxiety in school-age children in the ER and identify the relationship between caregivers' and school-age children's anxiety. Methods: This study employed a true experimental design using a post-test-only control group involving 66 children aged 6-12 years, randomly selected according to the inclusion and exclusion criteria. The intervention group, consisting of 33 children, received VR intervention, and the control group, consisting of 33 children, received standard care. Three respondents dropped out. Data analysis employed descriptive statistics, independent t tests, one-way ANOVA, and Pearson correlation analysis. Results: Most respondents were boys (39/66, 61.9%), accompanied by their mother (34/63, 54%), and had prior experience admitted to the ER (31/63, 49.2%). The anxiety in school-age children in the intervention group (mean 17.71, SD 3.013) was lower than that in the control group (mean 22.31, SD 3.167). There was a significant difference in the anxiety mean scores between the intervention group and the control group (t(61)=-5.907, P<.001). The mean (SD) of the caregivers' anxiety in the intervention and control groups were 46.06 (9.413) and 54.44 (9.112), respectively. There was a moderate relationship between caregivers' and school-age children's anxiety (r=.532, P<.001). Conclusions: This study has proven that VR can reduce school-age children's anxiety.
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How this classification was reachedexpand
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 | 0.002 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".