A test of psychotherapists' and nontherapists' recognition of correct and incorrect process in managing client resistance
Bibliographic record
Abstract
Abstract Objective Psychotherapist management of client resistance (i.e., opposition to change or the therapy process) has a significant impact on therapy outcomes. This study aimed at assessing psychotherapists' and nontherapists' abilities to recognise empirically supported correct and erroneous therapist responses to client resistance. Method Seventy‐eight therapists and 111 nontherapists completed a rapidly administered novel skills test, Therapist Errors in Resistance Management (TERM), assessing recognition of correct and erroneous resistance management. Results Participants were, on average, less than half as accurate at recognising therapist errors (recognised below chance levels) compared with correct therapist responses to resistance (recognised above chance levels). Therapists outperformed nontherapists in accurate recognition of therapist responding. However, among the therapist group, greater clinical experience did not predict superior recognition of therapist errors. Conclusion These results indicate that therapists and nontherapists alike may have difficulty detecting erroneous therapist responses to resistance, tending to mistake empirically contraindicated directive responses for correct responding. This may reflect the difficult and unintuitive nature of correct resistance responding and may suggest a need for increased training in process acuity and resistance management in therapist education.
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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 | 0.046 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".