Assessing Self-Critical Perfectionism in Clinical Depression
Bibliographic record
Abstract
Several facets of perfectionism have been strongly associated with depression and anxiety. Dunkley and Blankstein (2000) Dunkley, D. M. and Blankstein, K. R. 2000. Self-critical perfectionism, coping, hassles, and current distress: A structural equation modeling approach. Cognitive Therapy and Research, 24: 713–730. [Crossref], [Web of Science ®] , [Google Scholar] combined these maladaptive traits with self-criticism to create a general construct labeled self-critical perfectionism. In this study, we employed confirmatory factor analysis to evaluate a model for assessing self-critical perfectionism in a clinically depressed sample using scales from 3 instruments. Participants were 356 depressed adult outpatients who completed 2 multidimensional measures of perfectionism and a measure of self-criticism. A confirmatory factor model that separated a self-critical perfectionism construct from a more adaptive, achievement-striving component of perfectionism was supported. A composite scale assessing self-critical perfectionism demonstrated much larger correlations with distress measures compared to a composite scale assessing achievement striving and also showed evidence of discriminant validity. In this study, we provided further support for the valid assessment of self-critical perfectionism and extended evidence for its assessment to a clinically depressed sample.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".