Lost connection? Comparing group cohesion and treatment outcomes between videoconference and in-person cognitive behavioural group therapy for social anxiety disorder and other anxiety disorders
Bibliographic record
Abstract
Abstract Background: One of the most effective treatments for social anxiety disorder (SAD) is cognitive behavioural therapy (CBT). Prior research indicates group cohesion is connected to treatment success in group CBT for SAD (CBGT). Videoconference CBGT delivery is now common following the COVID-19 pandemic; however, research investigating treatment outcomes and group cohesion in videoconference CBGT for SAD is limited. Aims: The present study aimed to compare group cohesion in videoconference CBGT for SAD to group cohesion in both in-person CBGT for SAD and videoconference CBGT for other anxiety and related disorders. A secondary aim was to compare symptom reduction across all three groups. Method: Patients completed a 12-week CBGT program for SAD in-person (n=28), SAD via videoconference (n=46), or for another anxiety or related disorder via videoconference (n=100). At mid- and post-treatment patients completed the Group Cohesion Scale Revised (GCS-R), and at pre- and post-treatment patients completed the Social Phobia Inventory (SPIN, only in the SAD groups) and the Depression Anxiety Stress Scales (DASS-21). Results: Over the course of treatment, all three groups showed a significant increase in cohesion and a significant decrease in symptoms (ηp2 ranged from .156 to .562, all p<.001). Furthermore, analyses revealed no significant difference in cohesion scores between groups at both mid- and post-treatment. Conclusions: These results suggest that videoconference CBGT for SAD is similarly effective in facilitating cohesion and reducing symptoms compared with in-person delivery. Limitations of the study and implications for treatment 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".