The Resistance Vignette Task: Validating a rapid measure of therapist skill at managing resistance
Bibliographic record
Abstract
BACKGROUND: Therapist appropriate responsivity to client ambivalence and resistance is considered an important interpersonal skill to avoid disengagement and ensure a continued collaborative, productive process. The present study examined the predictive validity of the newly developed Resistance Vignette Task (RVT), a 10-item rapidly administered measure of therapist ability to appropriately respond to various presentations of client resistance. METHODS: Following a resistance management workshop, the concurrent and prospective predictive capacity of RVT scores were examined through test interviews with ambivalent simulators and volunteers. RESULTS: Prospectively, in test interviews with ambivalent interviewees, higher RVT scores immediately postworkshop were associated with significantly greater responsivity (appropriate responsivity and fewer responsivity errors) at 4-month follow-up. RVT scores at the 4-month follow-up point were also concurrently associated with significantly greater therapist responsivity and lower levels of interviewee resistance. CONCLUSIONS: These findings provide further validation for the RVT as a measure of therapist responsivity in vivo, in actual interviews by predicting and being concurrently associated with therapist performance in response to client resistance. Thus, the RVT holds promise in advancing therapist training, as well as research on resistance as it represents an efficient measure of this key therapist skill.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".