Feasibility of large group cognitive behavioural therapy education classes for anxiety disorders
Bibliographic record
Abstract
Rationale, aims and objectives: Cognitive behavioural therapy (CBT) is effective in treating anxiety disorders. In publically funded systems, CBT is not easily accessible due to long wait times. In order to provide quicker access, a large group CBT intervention was implemented in a tertiary care clinic. This study describes the design, feasibility and acceptability of such an intervention as part of stepped care for anxiety disorders. Method: The intervention followed a 2-session curriculum, consisting of 90 minutes classes providing didactic instruction on key CBT topics and self-management strategies. Classes accommodated 30 patients and were led by staff psychiatrists formally trained in CBT. A retrospective analysis of patients referred to the clinic during the first year of class implementation was performed to determine class completion rate, patient satisfaction (using a usefulness Likert Scale and Session Rating Scale [SRS]) and symptom trajectory (using the GAD-7).Results: The implementation of large group CBT classes reduced waiting times from approximately one year to approximately 3 months. One hundred and thirty-one patients were screened by the clinic, 88 of whom (67%) completed the intervention. Sixty-eight percent of patients rated the classes as useful; however, SRS findings indicated that only 46% of patients were satisfied. GAD-7 scores decreased by 1.57 (95% CI 0.2 to 2.95; SMD=0.24).Conclusions: This analysis contributes preliminary evidence that large group CBT education classes may be an acceptable means to reduce waiting times for CBT for anxiety disorders. Further controlled research is required to elucidate the benefit and cost effectiveness of such classes.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".