Virtual reality compared with<i>in vivo</i>exposure in the treatment of social anxiety disorder: A three-arm randomised controlled trial
Bibliographic record
Abstract
Background People with social anxiety disorder (SAD) fear social interactions and may be reluctant to seek treatments involving exposure to social situations. Social exposure conducted in virtual reality (VR), embedded in individual cognitive–behavioural therapy (CBT), could be an answer. Aims To show that conducting VR exposure in CBT for SAD is effective and is more practical for therapists than conducting exposure in vivo. Method Participants were randomly assigned to either VR exposure ( n = 17), in vivo exposure ( n = 22) or waiting list ( n = 20). Participants in the active arms received individual CBT for 14 weekly sessions and outcome was assessed with questionnaires and a behaviour avoidance test. (Trial registration number ISRCTN99747069.) Results Improvements were found on the primary (Liebowitz Social Anxiety Scale) and all five secondary outcome measures in both CBT groups compared with the waiting list. Conducting exposure in VR was more effective at post-treatment than in vivo on the primary outcome measure and on one secondary measure. Improvements were maintained at the 6-month follow-up. VR was significantly more practical for therapists than in vivo exposure. Conclusions Using VR can be advantageous over standard CBT as a potential solution for treatment avoidance and as an efficient, cost-effective and practical medium of exposure.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".