Does Perfectionism Predict Depression, Anxiety, Stress, and Life Satisfaction After Controlling for Neuroticism?
Bibliographic record
Abstract
Abstract. Neuroticism overlaps substantially with several perfectionism dimensions, depression, anxiety, stress, and life satisfaction. Accordingly, research testing whether perfectionism dimensions explain unique variance in these outcomes beyond neuroticism is needed. Research on cultural differences in perfectionism is also scarce. And it is especially unclear whether the link between perfectionism and psychological distress differs across individualistic and collectivistic cultures. Our study addressed these important gaps in knowledge. A sample of undergraduates from a traditionally individualistic culture (Canada; N = 449) and a traditionally collectivistic culture (China; N = 585) completed measures of self-oriented perfectionism, personal standards, socially prescribed perfectionism, concern over mistakes, doubts about actions, depression, anxiety, stress, and satisfaction with life. To test the incremental validity of perfectionism dimensions beyond neuroticism, as well as to test potential moderating effects of culture, four hierarchical regression analyses with interactions were conducted. Results supported the explanatory power of concern over mistakes and doubts about actions, beyond neuroticism and culture, in the prediction of depression, anxiety, and stress. As the first study to explore the incremental validity of perfectionism dimensions across undergraduates from traditionally individualistic and collectivistic cultures, our research both extends and clarifies understanding of the predictive power of perfectionism in important ways.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".