The Expanding Cognitive-Behavioural Therapy Treatment Umbrella for the Anxiety Disorders: Disorder-Specific and Transdiagnostic Approaches
Bibliographic record
Abstract
Cognitive-behavioural therapy (CBT) is an empirically supported treatment for anxiety disorders. CBT treatments are based on disorder-specific protocols that have been developed to target individual anxiety disorders, despite that anxiety disorders frequently co-occur and are comorbid with depression. Given the high rates of diagnostic comorbidity, substantial overlap in dimensional symptom ratings, and extensive evidence that the mood and anxiety disorders share a common set of psychological and biological vulnerabilities, transdiagnostic CBT protocols have recently been developed to treat the commonalities among the mood and anxiety disorders. We conducted a selective review of empirical developments in the transdiagnostic CBT treatment of anxiety and depression (2008-2013). Preliminary evidence suggests that theoretically based transdiagnostic CBT approaches lead to large treatment effects on the primary anxiety disorder, considerable reduction of diagnostic comorbidity, and some preliminary effects regarding the impact on the putative, shared psychological mechanisms. However, the empirical literature remains tentative owing to relatively small samples, limited direct comparisons with disorder-specific CBT protocols, and the relative absence of the study of disorder-specific compared with shared mechanisms of action in treatment. We conclude with a treatment conceptualization of the new transdiagnostic interventions as complementary, rather than contradictory, to disorder-specific CBT.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".