Patient perfectionism and clinician impression formation during an initial interview
Bibliographic record
Abstract
OBJECTIVES: The negative impact of pre-treatment patient perfectionism on therapeutic alliance and outcomes has been well documented. However, there is much to learn about how patient perfectionism impacts the development of the therapeutic alliance. Our study addressed this by examining the extent to which trait and self-presentational components of perfectionism influence clinician's perceptions of patients during an initial interview. DESIGN: = 36.2; 40 men) recruited from outpatient mental health clinics. Each patient had a one-on-one, semi-structured interview with a clinician that lasted approximately 50 min. METHOD: Patients completed self-report measures assessing trait perfectionism, perfectionistic self-presentation, and symptom distress before the interview. Patients were then invited to discuss reasons for seeking treatment and to reflect on the two most challenging situations in their lives in which they had not coped well. Following the interview, clinicians indicated their overall impressions of patients by responding to three self-report questions and rated patients' distress and hostility via nine adjectives. RESULTS: After controlling for patients' symptom distress, other-oriented perfectionism and non-display of imperfection had small positive relationships with clinician-rated hostility; self-oriented perfectionism, socially prescribed perfectionism, and non-disclosure of imperfection had small-to-moderate negative relationships with clinician impressions. Additionally, path analysis revealed other-oriented perfectionism and non-display of imperfection indirectly predicted less favourable clinician impressions through clinician-rated hostility. CONCLUSIONS: Findings highlight the importance of evaluating and addressing trait and self-presentation components of perfectionism early in the therapeutic process. PRACTITIONER POINTS: Higher levels of other-oriented perfectionism and non-display of imperfection were associated with greater clinician-rated hostility during an initial interview. Patient hostility mediated the relationship between patients' other-oriented perfectionism, non-display of imperfection, and less favourable clinician impressions. Our study highlights the importance of assessing and attending to patient perfectionism and displays of hostility during the earliest stages of therapeutic contact.
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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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| 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".