Treatment of Storm Fears Using Virtual Reality and Progressive Muscle Relaxation
Bibliographic record
Abstract
BACKGROUND: The present study examined the efficacy of virtual reality (VR) exposure therapy for treating individuals with storm fears by comparing a one-session VR exposure treatment with a one-session progressive muscle relaxation (PMR) and psychoeducation session. AIMS: It was predicted that there would be a reduction in storm-related fear post-treatment for individuals in both conditions, but that this reduction would be greater for those in the VR exposure condition. It was predicted that improvements would be maintained at 30-day follow-up only for those in the VR exposure condition. METHOD: Thirty-six participants each received one of the two treatment conditions. Those in the PMR treatment group received approximately 30 minutes of PMR and approximately 15 minutes of psychoeducation regarding storms. Those in the VR treatment group received approximately 1 hour of VR exposure. Additionally, participants were asked to complete a pre-treatment and post-treatment 5-minute behavioural approach test to assess changes in storm fears. They were also asked to complete a measure assessing storm phobia. RESULTS: There was a significant interaction between treatment group and self-reported fear at post-treatment, such that fear decreased for both groups, although the reduction was stronger in the VR group. Results also showed that reductions in storm fear were maintained at 30-day follow-up for both groups. CONCLUSIONS: Although this study used a small non-clinical sample, these results offer preliminary support for the use of VR exposure therapy in the treatment of storm-related fear.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".