Preliminary Research on the Efficacy of Virtual Reality Exposure Therapy to Treat Driving Phobia
Bibliographic record
Abstract
This article presents a review of preliminary research of two studies of the efficacy of virtual reality exposure therapy (VRET) to treat driving phobia. Study 1 describes a case study of a patient who completed a 7-day baseline followed by three sessions of VRET. Her peak anxiety decreased within and across sessions. At the post-treatment assessment, her phobic-related symptoms had diminished and she no longer met diagnostic criteria for driving phobia. Clinical improvement was maintained at 1-, 3-, and 7-month follow-up. In study 2, a multiple baseline across-subjects design was used to treat five patients over eight weekly VRET sessions. Visual and statistical analyses showed clear improvement in driving anxiety and avoidance in three patients between pre- and post-treatment assessments, and they no longer met criteria for driving phobia. There was marginal improvement in one patient, and the remaining individual showed no treatment gains. There was negligible change in actual driving frequency for any of the patients. Some gains were lost at the follow-up, particularly for the two individuals with poorer treatment responses. The results from these preliminary studies suggest that VRET may be a promising intervention for treating driving phobia. Avenues for improving treatment outcome are discussed.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".