An analysis of differential improvement by diagnosis in group transdiagnostic cognitive behaviour therapy for anxiety disorders
Bibliographic record
Abstract
INTRODUCTION: Anxiety disorders are the most prevalent among the mental health disorders and have a negative impact on an individual's life. Cognitive behaviour therapy (CBT) is documented as the most effective treatment for anxiety disorders. However, challenges associated with implementing diagnosis-specific CBT have led to transdiagnostic approaches of CBT (tCBT). tCBT uses a single protocol with core elements of CBT for treatment of anxiety disorders broadly. The aim of the current study is to examine whether participants with different principal anxiety diagnoses demonstrate similar anxiety reduction. METHODS: The current study involved a secondary analysis of 117 participants randomly allocated to receive tCBT for anxiety disorders in a pragmatic randomised effectiveness trial. Beck Anxiety Inventory (BAI) and Clinician Severity Ratings (CSR) scales were administered at pre- and post-treatment and one-year follow-up, while the Anxiety Disorder Diagnostic Questionnaire - Weekly (ADDQ-W) was administered each session. RESULTS: Mixed-factorial analyses of variance (ANOVAs) indicated that participants with GAD, SAD and PD/A improved to post-treatment and maintained to follow-up, with no differential improvement across principal diagnoses. Mixed effect regression modelling of session by session measures indicated non-differential negative slopes across principal diagnoses of GAD, SAD and PD/A. CONCLUSION: Overall, results indicate that group tCBT for anxiety disorders shows equal effectiveness for GAD, PD/A, and SAD in real-world conditions.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".