Virtual Reality Therapy Versus Cognitive Behavior Therapy for Social Phobia: A Preliminary Controlled Study
Bibliographic record
Abstract
Social phobia is one of the most frequent mental disorders and is accessible to two forms of scientifically validated treatments: anti-depressant drugs and cognitive behavior therapies (CBT). In this last case, graded exposure to feared social situations is one of the fundamental therapeutic ingredients. Virtual reality technologies are an interesting alternative to the standard exposure in social phobia, especially since studies have shown its usefulness for the fear of public speaking. This paper reports a preliminary study in which a virtual reality therapy (VRT), based on exposure to virtual environments, was used to treat social phobia. The sample consisted of 36 participants diagnosed with social phobia assigned to either VRT or a group-CBT (control condition). The virtual environments used in the treatment recreate four situations dealing with social anxiety: performance, intimacy, scrutiny, and assertiveness. With the help of the therapist, the patient learns adapted cognitions and behaviors in order to reduce anxiety in the corresponding real situations. Both treatments lasted 12 weeks, and sessions were delivered according to a treatment manual. Results showed statistically and clinically significant improvement in both conditions. The effect-sizes comparing the efficacy of VRT to the control traditional group-CBT revealed that the differences between the two treatments are trivial.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".