What Lies Beyond the Superordinate Trait Perfectionism Factors? The Perfectionistic Self-Presentation and Perfectionism Cognitions Inventory Versus the Big Three Perfectionism Scale in Predicting Depression and Social Anxiety
Bibliographic record
Abstract
Extreme perfectionism has been linked with distress and dysfunction. This association is reflected by the recent development of the Big Three Perfectionism Scale (BTPS), which has superordinate trait-based scales that assess 3 broad elements-rigid, self-critical, and narcissistic perfectionism. We examined psychometric features of the BTPS as well as the links between the BTPS and indexes of distress. A sample of 602 undergraduates completed the BTPS, the Perfectionistic Self-Presentation Scale, the Perfectionism Cognitions Inventory, and measures of social anxiety and depression. Support was obtained for the psychometric qualities of the BTPS. All 3 superordinate trait factors were associated with social anxiety and depression. Analyses also established that rigid perfectionism, self-critical perfectionism, and narcissistic perfectionism are associated with perfectionistic cognitions and perfectionistic self-presentation. In addition, the results of a series of regression analyses established that perfectionistic self-presentation and perfectionistic cognitions accounted for significant unique variance in distress beyond the variance attributable to rigid, narcissistic, and self-critical perfectionism. Overall, our results suggest that the BTPS has significant promise as a predictor of various forms of dysfunction, but the cognitive and self-presentational aspects of the perfectionism construct are also uniquely relevant and not redundant with the BTPS superordinate trait factors.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".