The Multidimensional Assessment of Perfectionistic Automatic Thoughts
Bibliographic record
Abstract
In the current article, we comment on a recent article by Stoeber, Kobori, and Brown that provided evidence suggesting that a multidimensional approach to perfectionistic cognitions is superior to a unidimensional approach in predicting maladjustment. They also showed with their data from a university student sample that our Perfectionism Cognitions Inventory has multiple factors in contrast to our unidimensional approach. Our commentary focuses primarily on the issue of whether the Perfectionism Cognitions Inventory should be considered unidimensional versus multidimensional and outlines concerns about how perfectionism cognition factors should be used and interpreted. Although there are serious interpretive problems inherent in existing multidimensional measures of perfectionism cognitions, it is apparent that a cognitive approach is an important and viable supplement to the extensive focus on the trait multidimensional perfectionism that is currently in vogue. We conclude by discussing the potential clinical uses of cognitive assessments of perfectionism.
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.016 | 0.015 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".