Predictors of Skill in Resistance Management in Psychotherapy
Bibliographic record
Abstract
Appropriately responsive management of resistance in psychotherapy remains a foundational skill that is associated with positive client outcomes (Westra & Norouzian, 2018). Despite this, little is known about which individual differences contribute to successful management of resistance. Findings suggest that psychotherapy performance does not improve with experience (Goldberg et al., 2016), that psychotherapists lack humility due to positively biased self-assessment (Walfish et al., 2012), and that difficult moments in psychotherapy may dysregulate therapist emotions (Grecucci & Sanfey, 2014). This thesis therefore had two primary aims: 1) to identify whether psychotherapy training experience (n = 98 untrained participants and n = 76 trained participants) was associated with resistance management, and 2) to identify whether humility and difficulties regulating emotions in trained individuals (n = 76) were associated with resistance management (i.e., as operationalized using the Resistance Vignette Task – RVT; Westra et al., 2021). Results indicated that trained individuals performed significantly better on the RVT than untrained individuals, however, years of experience within the trained sample were not associated with RVT scores. Furthermore, humility and difficulties regulating emotions were each independently associated with resistance management in the trained group. These findings suggest the possibility of improving training to focus on key skills, such as resistance management, through supporting humility and emotion regulation in training. By identifying ways to improve therapist skill in resistance management (i.e., by introducing skill training and promoting humility and emotion regulation in therapists), client outcomes may subsequently improve.
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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 | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".