The interpersonal context of client motivational language in cognitive–behavioral therapy.
Bibliographic record
Abstract
Previous research has found that client motivational language (especially arguments against change or counterchange talk; CCT) in early therapy sessions is a reliable predictor of therapy process and outcomes across a broad range of treatments including cognitive-behavioral therapy (CBT). Existing studies have considered the general occurrence of CCT, but the present study differentiated 2 types of CCT in early CBT sessions for 37 clients with generalized anxiety disorder: (a) statements that are uttered to express ambivalence regarding change versus (b) statements that are intended to oppose the therapist or therapy. Two process coding systems were used to accomplish this differentiation. Findings indicated that a higher number of CCT statements that occurred in the presence of resistance (opposition to the therapist or therapy) were a substantive and consistent predictor of lower homework compliance and poorer outcomes, up to 1 year posttreatment. Moreover, when both types of CCT were considered together, only opposition CCT was related to outcomes, and ambivalent CCT was not significantly predictive of proximal and distal outcomes. These findings suggest that the interpersonal context in which CCT statements occur may be critically important to their predictive capacity. More broadly, the findings of this study have implications for the future study of client motivational language and underscore the clinical importance of detecting opposition CCT.
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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 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".