Client attachment to therapist: Relation to client personality and symptomatology, and their contributions to the therapeutic alliance.
Bibliographic record
Abstract
This study examined the relation of client attachment to the therapist to diverse facets of the therapeutic alliance, client personality, and psychopathological symptoms, as well as the relative importance of therapeutic attachments, personality, and symptomatology in predicting the alliance. Eighty clients in ongoing therapy completed measures of client attachment to therapist (CATS), personality (6FPQ), psychopathological symptoms (BSI), and therapeutic alliance (WAI-Short, CALPAS, HAQ). Secure and Avoidant-Fearful attachment to the therapist correlated positively and negatively, respectively, with total and subscale alliance scores. Preoccupied-Merger therapeutic attachment was unrelated to the alliance. Exploratory analyses suggested however that the relationship between Preoccupied-Merger attachment and the alliance was moderated by the extent to which clients were distressed. Clients' therapeutic attachments were unrelated to basic personality dimensions. Preoccupied-Merger attachment to the therapist correlated significantly with several symptom dimensions. Clients' therapeutic attachments emerged as superior and more consistent predictors, relative to client personality and symptomatology, of the therapeutic alliance.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".