Addressing the Complexity of Perfectionism in Clinical Practice
Bibliographic record
Abstract
Perfectionism, defined as the need to be or appear to be perfect, is a multidimensional personality construct that makes individuals vulnerable to a host of clinical problems including depression, anxiety, personality and eating disorders, as well as suicide behaviors, interpersonal dysfunction, and difficulties achieving successful therapeutic outcome. Given the detrimental associations with perfectionism, it is crucial that mental health professionals be familiar with and able to identify patients presenting with perfectionistic characteristics. The purpose of this article is to provide an overview of a comprehensive conceptualization of perfectionism and its assessment and treatment in clinical practice based on the psychodynamic and interpersonal perspective of Hewitt et al. (2017). This article presents conceptual models of perfectionism, assessment measures, treatment considerations and challenges, and a case example of a patient with perfectionism. Through understanding the nature and treatment of perfectionism, clinicians can broaden and strengthen their knowledge and skill in helping patients struggling with perfectionistic difficulties and the attendant symptoms, syndromes, and disorders.
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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 | 0.024 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".