The Destructiveness of Perfectionism Revisited: Implications for the Assessment of Suicide Risk and the Prevention of Suicide
Bibliographic record
Abstract
Although perfectionism is recognized as a factor that is linked with suicide, we maintain that the role of perfectionism as an amplifier of the risk of suicide has been underestimated due to several factors. In the current article, contemporary research on the role of perfectionism in suicide is reviewed and summarized. Several themes are addressed, including: (a) consistent evidence linking suicide ideation with chronic exposure to external pressures to be perfect (i.e., socially prescribed perfectionism); (b) the roles of perfectionistic self-presentation and self-concealment in suicides that occur without warning; and (c) how perfectionism contributes to lethal suicide behaviors. We also summarize data showing consistent links between perfectionism and hopelessness and discuss the need for a person-centered approach that recognizes the heightened risk for perfectionists who also tend to experience hopelessness, psychache, life stress, overgeneralization, and a form of emotional perfectionism that restricts the willingness to disclose suicidal urges and intentions. It is concluded that when formulating clinical guidelines for suicide risk assessment and intervention and public health approaches to suicide prevention, there is an urgent need for an expanded conceptualization of perfectionism as an individual and societal risk factor. We also discuss why it is essential to design preventive programs tailored to key personality features with specific components that should enhance resilience and reduce levels of risk among perfectionists who hide behind a mask of apparent invulnerability.
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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 | 0.018 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.014 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.010 |
| 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".