The light and dark sides of perfectionism : implications for health and well-being
Bibliographic record
Abstract
The present work presents two studies that examined the association of perfectionism, operationally defined by Hewi t t and Fl e t t ' s (1991) multidimensional mode l of perfectionism, with health and subjective well-being (SWB). The underlying question of this research was whether perfectionism could be beneficial as well as detrimental to health and well-being, as this is one of the mos t highly debated questions in the current literature. In samples of relatively healthy university students (n = 538) and community adults suffering from various chronic illnesses (n = 772), results from Study One indicated that socially prescribed perfectionism (SPP) is directly associated wi th poor e r he a l th and well-being. Results further showed f rom a personcentered perspective that there is a l a rge group of individuals wi th high levels of SPP and that i t is indeed these individuals who reported the poorest health and lowe s t levels of well-being. Other-oriented perfectionism was found to be unrelated to health and SWB. Findings revealed that when perfectionism is self-imposed (i.e., self-oriented perfectionism; SOP), i t is neither healthy nor unhealthy in an absolute sense. From the variable-centered perspective, this conclusion was supported by the f a c t tha t SOP was associated wi th both positive (e.g., be t t e r mental health and highe r levels of SWB in the student sample), and nega t ive correlates (e.g., higher levels of negative affect, stress, and neuroticism in both samples). Evidence f rom the chronically-ill sample further substantiated this conclusion by showing that there may be an optimal level of SOP, because mode r a t e levels of SOP we r e found to be associated with be t t e r health and highe r levels of SWB, whereas levels tha t we r e too low or too high we r e found to be associated with poor e r health and lowe r levels of SWB. Findings f rom the person-centered approach we r e particularly informative, in that they not only demonstrated tha t unique profiles of
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".