The Impact of Perfectionistic Self-Presentation on the Cognitive, Affective, and Physiological Experience of a Clinical Interview
Bibliographic record
Abstract
Perfectionistic self-presentation is proposed as a deleterious interpersonal style that has an influence in clinical contexts that involves promoting a public image of perfection and avoiding displays and self-disclosures of imperfections. A sample of 90 clinical patients taking part in a clinical interview were assessed in terms of their levels of perfectionistic self-presentation and trait perfectionism and their affective, cognitive, and physiological reactions. Perfectionistic self-presentation dimensions were associated with (1) greater distress before and after the interview, (2) negative expectations and greater threat prior to the interview, and (3) post-interview dissatisfaction. Analyses of physiological data found that perfectionistic self-presentation was associated with higher levels of heart rate when discussing past mistakes, and, as expected, the need to avoid disclosing imperfections predicted higher levels of and greater change in heart rate when discussing past mistakes. Analyses that controlled for trait perfectionism and emotional distress showed that the need to avoid disclosing imperfections was a unique predictor of (1) appraisals of the interviewer as threatening before the interview and as dissatisfied after the interview; (2) negative pre and post self-evaluations of performance; and (3) greater change in heart rate when discussing mistakes. Perfectionistic self-presentation is discussed as an interpersonal style that can influence therapeutic alliance and treatment success.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".