Cognitive Behavioural Therapy Outcomes on Clinical Perfectionism: A Scoping Review
Bibliographic record
Abstract
Introduction: Perfectionism has been identified as a transdiagnostic process, associated with various psychopathologies. Clinical perfectionism, the dysfunctional or maladaptive element of perfectionism, is defined as relying on meeting self-imposed standards, despite any adverse consequences. When addressing clinical perfectionism, cognitive behavioural therapy (CBT) is a popular intervention. However, the current literature looking at CBT outcomes on perfectionism is diverse and includes a number of delivery methods, comorbid diagnoses, and sample populations. Thus, this scoping review aims to tease apart and summarize the literature surrounding CBT outcomes on clinical perfectionism. Methods: A systematic search was conducted in University of Toronto Libraries, APA PsycInfo (Ovid), APA PsycInfo (ProQuest), PubMED, and MEDLINE using relevant search terms. Article screening and extraction was completed in three stages: 1) title/abstract, 2) full text review, 3) extraction. A total of 13 studies were included in the scoping review. Results: When looking at CBT outcomes on clinical perfectionism, there was significant reductions in perfectionism measures, including the CPQ and FMPS, which was also clinically significant and lead to reliable change. Additional outcomes include reduction in depression, anxiety, and eating disorder psychopathology. CBT interventions were generally effective and tolerated by participants, increasing reported quality of life, satisfaction, self-esteem, and self-compassion. Conclusion: Overall, the use of CBT on clinical perfectionism leads to positive results and is a feasible method to treat perfectionism in a variety of patients, including adults with anxiety disorders, depression, eating disorders, and obsessive-compulsive disorder. Future studies should investigate this topic with more diverse population and compare intervention lengths.
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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 | 0.013 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".