The comparison between CBT focused on perfectionism and CBT focused on emotion regulation for individuals with depression and anxiety disorders and dysfunctional perfectionism: a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: There is considerable evidence indicating that similar aetiological and maintenance processes underlie depressive and anxious psychopathology. According to the literature, perfectionism and emotion regulation are two transdiagnostic constructs associated with symptoms of emotional disorders. AIMS: This study is the first randomized controlled trial comparing the efficacy of cognitive behavioural therapy for perfectionism (CBT-P) and the unified protocol for the transdiagnostic treatment of emotional disorders (UP). METHOD: Seventy-five participants with a range of depressive and anxiety disorders and elevated perfectionism were randomized to three conditions: CBT-P, UP or a waitlist control (WL). RESULTS: Repeated measures ANOVA indicated that the treatment groups reported a significantly greater pre-post reduction in the severity of symptoms of disorders, as well as a significantly greater pre-post increase in quality of life, all with moderate to large effect sizes compared with the WL group. Treatment gains were maintained at 6-month follow-up. The CBT-P group reported a significantly greater pre-post reduction in perfectionism compared with UP, and the UP group reported a significantly greater pre-post improvement in emotion regulation compared with CBT-P. CONCLUSIONS: Findings support CBT for perfectionism and regard UP as efficacious treatments for individuals with depression and anxiety disorders who also have dysfunctional perfectionism. It appears that perfectionism cannot be a serious obstacle to UP. As this is a preliminary study and has some limitations, it is recommended that further research be conducted.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".