Third-wave therapy with virtual reality exposure: Transdiagnostic proof of concept for the treatment of social anxiety
Bibliographic record
Abstract
Third-wave therapies and Virtual Reality (VR) exposure are two promising interventions for Social Anxiety (SA) disorder. These interventions are not new, but what is innovative is the combination of them. The main objective of this study was to evaluate the preliminary efficacy of a treatment for SA that combines the best of third-wave therapies with the advantages of immersive VR exposure. Three experimental groups with SA disorder were recruited: SA only, SA with comorbidity, and SA with psychosis (n = 50; 64 % female; mean age 22.98). A control group was also recruited: SA with and without comorbidities (n = 25; 84 % female; mean age 24.88). Experimental participants received eight weekly treatment sessions of about 60 min each. At the end of the study, the control group was provided with a list of telephone helplines and clinics for the treatment of SA. The results suggest that the intervention was effective in reducing SA symptoms with large effect sizes, irrespective of the presence or absence of comorbid disorders. The impact of the intervention was also observed on other clinical variables: general anxiety, depression, intolerance of uncertainty, cognitive distortions, mindfulness skills, and non-adaptative emotional regulation. Significant predictors of treatment change were linked to improvements in intolerance of uncertainty, experiential avoidance, and the frequency of exposure exercises. The results support the combined use of VR exposure and third-wave therapy for the treatment of SA. Further studies are needed to confirm the efficacy and investigate the implementation considerations of combining these interventions for SA disorder.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".