A Person-Oriented Approach to Multidimensional Perfectionism: Perfectionism Profiles in Health and Well-Being
Bibliographic record
Abstract
Advances in understanding of the perfectionism construct have been limited by an almost exclusive reliance on a variable-centered approach. This study utilized a person-oriented approach to examine Hewitt and Flett’s conceptualization of multidimensional perfectionism in relation to health and well-being. Levels of conscientiousness, extraversion, and neuroticism were also assessed. Cluster analyses were employed to examine within-person configurations of self-oriented, other-oriented, and socially prescribed perfectionism (SPP) in university students ( n = 538) and adults with chronic illness ( n = 773). Five unique configurations were found in both samples and three clusters replicated across samples. “Extreme perfectionists” with high scores across all perfectionism dimensions reported relatively poor physical health, psychological health, psychosocial resources, and well-being along with elevated neuroticism and conscientiousness. A group distinguished by elevated SPP also reported relatively poorer outcomes along with elevated neuroticism and lower conscientiousness. In contrast, “nonperfectionists” reported relatively elevated levels of health and well-being. These profiles differed in their links with health and well-being even after taking into account key differences in conscientiousness and neuroticism. Our results illustrate the importance of employing a person-oriented approach to the study of multidimensional perfectionism, especially as it relates to physical health, mental health, and subjective well-being.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".