The prevalence of perfectionism and positive mental health in undergraduate students
Bibliographic record
Abstract
The purpose of this cross-sectional survey was to assess the relationship between mental health and perfectionist personality styles within Dalhousie University’s undergraduate psychology program (N = 191). Positive mental health is characterized by high social, emotional, and psychological functioning in everyday life. Perfectionism has traditionally been studied as a correlate of poor mental health, although relatively recent research has offered a reconceptualization wherein the adaptiveness of perfectionism can support positive mental health. In particular, the perfectionist personality style may be categorized as three types: non-perfectionist, maladaptive perfectionist, and adaptive perfectionist. We classified participants based on their perfectionist personality style and assessed the distribution of mental health level and variation of mental health scores across the different perfectionist personality styles. Overall, we found a pattern of high mental health scores in adaptive perfectionists, moderate mental health scores in non-perfectionists, and low mental health scores in maladaptive perfectionists, suggesting that mental health varies systematically with type of perfectionism. Our findings demonstrate that perfectionism can be an adaptive personality style and positively relate to mental health. Our study supports the reconceptualized definition of perfectionism as a potentially adaptive personality style.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".