Are Perfectionism Dimensions Vulnerability Factors for Depressive Symptoms after Controlling for Neuroticism? A Meta–analysis of 10 Longitudinal Studies
Bibliographic record
Abstract
Extensive evidence suggests neuroticism is a higher–order personality trait that overlaps substantially with perfectionism dimensions and depressive symptoms. Such evidence raises an important question: Which perfectionism dimensions are vulnerability factors for depressive symptoms after controlling for neuroticism? To address this, a meta–analysis of research testing whether socially prescribed perfectionism, concern over mistakes, doubts about actions, personal standards, perfectionistic attitudes, self–criticism and self–oriented perfectionism predict change in depressive symptoms, after controlling for baseline depression and neuroticism, was conducted. A literature search yielded 10 relevant studies (N = 1,758). Meta–analysis using random–effects models revealed that all seven perfectionism dimensions had small positive relationships with follow–up depressive symptoms beyond baseline depression and neuroticism. Perfectionism dimensions appear neither redundant with nor captured by neuroticism. Results lend credence and coherence to theoretical accounts and empirical studies suggesting perfectionism dimensions are part of the premorbid personality of people vulnerable to depressive symptoms. Copyright © 2016 European Association of Personality Psychology
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.031 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| 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".