Therapist responsivity during disagreement in psychotherapy
Bibliographic record
Abstract
Abstract Objective Therapist responsivity is recognised as an important factor for improved psychotherapy process and outcome. Previous research (Aviram et al., 2016) has found that increased therapist responsivity during key moments (e.g., disagreements) is particularly impactful to post‐treatment outcome. However, global scores of the observational coding system used in prior studies fail to capture more precise micromoments and specific therapist responsivity within disagreement episodes that may contribute to outcome. In response, this study analysed therapy disagreement episodes with more precise coding measures that capture moment‐to‐moment sequences of therapist and client utterances. Method Sixty disagreement episodes (segment beginning with clear client disagreement with therapist and ending once they shifted to a new topic) previously abstracted from early working phase sessions of a randomised controlled trial (RCT) of cognitive behavioural therapy with and without integrated motivational interviewing (Hara et al., 2022) were utilised. To gauge responsivity within these episodes, two therapist behaviours (demand and support) were examined in response to specific moments of client counter‐change talk (CCT) statements using microlevel, moment‐to‐moment coding systems. This resulted in indices of appropriate (CCT‐Support) and inappropriate/errors in responsivity (CCT‐Demand). Responsivity indices were compared with the gold standard observational coding measure for managing ambivalence and resistance, the Motivational Interviewing Treatment Integrity scale (MITI). Results While appropriate responsivity did not predict outcome, responsivity errors significantly predicted poorer outcome at one year post‐treatment. Additionally, the capacity of responsivity errors for predicting outcome was equivalent to that obtained from the global observational measure. Conclusion These findings have significant implications for training by emphasising the need for therapists to be sensitive to context and to acquire skill at detecting and responsively managing specific moments to avoid responsivity errors and improve therapy outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 teacher head, 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".