Integrated group treatment for anxiety disorders: a transdiagnostic effectiveness and feasibility study in a mental health hospital setting
Bibliographic record
Abstract
Objective The development of transdiagnostic cognitive-behavioural therapy (T-CBT) is a promising avenue for the treatment of anxiety disorders. This study aimed at evaluating the feasibility and effectiveness of a group T-CBT, developed by practising clinicians, for patients with principal and comorbid anxiety disorders in a pragmatic quasi-experimental trial.Method Clinician-rated and self-rated measures were used to assess principal (anxiety) and secondary (depression, daily functioning, quality of life) outcomes. Forty-two participants from an anxiety outpatient clinic of a mental health hospital with at least one primary anxiety disorder took part in the study. The intervention consisted of 8 to 10 weekly group T-CBT sessions of 120 minutes.Results Among the 42 participants recruited, 37 completed baseline measures. Attrition was high (35%) as only 24 participants completed the treatment as intended. Significant improvements were observed on anxiety, depression, and daily functioning as well as physical and psychological quality of life measures. Gains were maintained up to three months after the end of treatment.Conclusion Findings support the effectiveness of a group T-CBT for anxiety disorders in an outpatient clinic. However, feasibility was only partially supported highlighting the need for further research on the transportability of group T-CBT in to clinical practice.KEY POINTS What is already known about this topic: (1) Transdiagnostic group CBT is an effective treatment for anxiety disorders.(2) Few studies have examined the effectiveness of transdiagnostic group CBT in naturalistic settings.(3) Few studies have examined the effectiveness of treatments as implemented by clinicians in routine clinical practice.What this paper adds: (1) Eight to ten sessions of transdiagnostic group CBT developed by practising clinicians seemed sufficient in improving anxiety and depressive symptoms, daily functioning, and quality of life in patients from an outpatient anxiety disorders clinic in a hospital setting.(2) Patients that completed treatment reported significantly more impairment in daily functioning before treatment than participants who dropped out.(3) High attrition rates suggest that organisational factors need to be considered while implementing evidence-based treatments into routine clinical practice.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".