Improving adherence to a web-based cognitive-behavioural therapy program for social anxiety with group sessions: A randomised control trial
Bibliographic record
Abstract
Individuals with social anxiety disorder (SAD) commonly receive non-evidence based, ineffective treatments. Cognitive behaviour therapy (CBT) has been demonstrated to be the gold standard treatment for treating SAD. Scalable web-based CBT programs ensure evidence-based treatment procedures, but low treatment adherence remains problematic. This study aimed to test whether adding group sessions to a fully automated web-based CBT program, Overcome Social Anxiety (OSA), would increase treatment adherence. A total of 69 participants were provided access to a web-based program, and randomly allocated to three conditions: 1) An experimental condition involving an addition of three online group psychoeducation sessions; 2) a placebo condition involving an addition of three online progressive muscle relaxation (PMR) group sessions, or 3) a control condition where participants did not receive group sessions. Adherence was operationalised as number of OSA modules completed. Treatment adherence significantly differed between the conditions. On average, participants assigned to the placebo condition completed significantly more of the program compared to those in the control condition. Further, all conditions produced a significant improvement in BFNE and QOLS. No significant difference in treatment efficacy was found between groups on the SIAS, BFNE or QOLS. The current results indicate PMR can improve treatment adherence for scalable social anxiety interventions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| 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 | 0.003 | 0.003 |
| 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".