Dynamic-relational group treatment for perfectionism: Informant ratings of patient change.
Bibliographic record
Abstract
Although now there is accumulating research on the effectiveness of psychotherapy for perfectionism, this research has been based almost exclusively on self-report data. In this article, we describe analyses from the University of British Columbia Perfectionism Treatment Study assessing close other informant ratings of change in perfectionism traits and perfectionistic self-presentation. A total of 61 close other informants of patients who participated in a 10-week dynamic-relational treatment for perfectionism completed measures of patient trait and self-presentational facets of perfectionism at pretreatment, at posttreatment, and at a 4-month follow-up. In support of the effectiveness of the treatment, we found that close other measures of patients' self-oriented perfectionism, other-oriented perfectionism, and all three facets of perfectionistic self-presentation were significantly reduced at posttreatment and follow-up. Close other measures of patients' socially prescribed perfectionism did not show change over the course of treatment and follow-up. The findings are discussed in terms of the effectiveness of the dynamic-relational treatment of perfectionism and the utility of extending research by including close other measures of change in treatment-outcome research. (PsycInfo Database Record (c) 2020 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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".