The perniciousness of perfectionism: A meta‐analytic review of the perfectionism–suicide relationship
Bibliographic record
Abstract
OBJECTIVE: Over 50 years of research implicates perfectionism in suicide. Yet the role of perfectionism in suicide needs clarification due to notable between-study inconsistencies in findings, underpowered studies, and uncertainty about whether perfectionism confers risk for suicide. We addressed this by meta-analyzing perfectionism's relationship with suicide ideation and attempts. We also tested whether self-oriented, other-oriented, and socially prescribed perfectionism predicted increased suicide ideation, beyond baseline ideation. METHOD: Our literature search yielded 45 studies (N = 11,747) composed of undergraduates, medical students, community adults, and psychiatric patients. RESULTS: Meta-analysis using random effects models revealed perfectionistic concerns (socially prescribed perfectionism, concern over mistakes, doubts about actions, discrepancy, perfectionistic attitudes), perfectionistic strivings (self-oriented perfectionism, personal standards), parental criticism, and parental expectations displayed small-to-moderate positive associations with suicide ideation. Socially prescribed perfectionism also predicted longitudinal increases in suicide ideation. Additionally, perfectionistic concerns, parental criticism, and parental expectations displayed small, positive associations with suicide attempts. CONCLUSIONS: Results lend credence to theoretical accounts suggesting self-generated and socially based pressures to be perfect are part of the premorbid personality of people prone to suicide ideation and attempts. Perfectionistic strivings' association with suicide ideation also draws into question the notion that such strivings are healthy, adaptive, or advisable.
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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 | 0.013 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.014 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".