Pathological narcissism’s impact on psychodynamic group therapy for perfectionism.
Bibliographic record
Abstract
Several decades of theory suggest that pathological narcissism (PN) may limit psychotherapy success, but empirical evidence for such theories is limited and mixed. In addition, it has been proposed that individuals with high levels of PN may benefit more from supportive compared to interpretive psychodynamic therapies, but no studies thus far have investigated this question empirically. As such, our study aimed to extend past research by investigating (a) whether higher levels of pretreatment PN predict poorer treatment outcome and (b) whether the type of psychodynamic therapy (supportive or interpretive therapy) moderates these findings, in a sample of patients undergoing group psychodynamic psychotherapy for perfectionism. The sample was drawn from the University of British Columbia Perfectionism Treatment Study II (Hewitt et al., 2023) and consisted of 80 treatment-seeking adults with elevated perfectionism. Contrary to expectations, multilevel and multiple regression analyses showed that pretreatment PN did not significantly predict posttreatment changes in symptom severity, life satisfaction, or work and social impairment. We also did not find that either grandiose or vulnerable narcissism predicted likelihood of patient dropout. Finally, treatment type did not moderate the relationship between pretreatment PN and treatment outcome, suggesting that, contrary to our hypotheses, PN does not impact treatment outcome regardless of the interpretive nature of the psychodynamic group therapy. These results, taken together with past findings, suggest that PN may not be associated with poorer psychotherapy outcomes in certain contexts, such as in the case of supportive or interpretive psychodynamic group psychotherapy for perfectionism. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".