Perfectionism and therapeutic alliance: a review of the clinical research
Bibliographic record
Abstract
In this review, we synthesize findings regarding the relationship between perfectionism and therapeutic alliance, most of which come from analyses by Blatt and colleagues. Results suggest what follows. First, patients' initial level of perfectionism negatively affects patients' bond with therapists and perception of therapists' Rogerian attributes (empathy, congruence, and regard) early in treatment and engagement in therapy later in treatment. Second, therapists' contribution to alliance is not seemingly affected by patients' initial perfectionism level. Third, individual patients of therapists who are perceived on average by their patients to be higher on Rogerian attributes experience greater decreases in perfectionism and symptoms. Fourth, more positive perceptions of therapists' Rogerian attributes early in treatment lead to greater symptom decrease for patients with moderate perfectionism. Fifth, greater early patient engagement in therapy is related to greater decrease in perfectionism, but a strong relationship with the therapist may be necessary for an accompanied greater decrease in symptoms. The relationship between pre-treatment perfectionism and alliance is partially explained by higher levels of hostility and lower levels of positive affect. Sixth, the relationship between pre-treatment perfectionism and outcome is almost entirely explained by level of patient contribution to alliance and satisfaction with social network, highlighting the importance of focusing on social functioning for patients with high perfectionism (both in and outside of the session). Limitations include that most of the findings are from analyses of one large data set and a range of measurement issues. Future research should utilize different measures, perspectives, and populations and examine specific session process.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".