Rolling with resistance: A client language analysis of deliberate practice in continuing education for psychotherapists
Bibliographic record
Abstract
Abstract Continuing education workshops have been criticised for focusing on knowledge rather than skill acquisition by a focus on didactic teaching methods. A recent randomised controlled trial conducted by Westra et al. (2020) demonstrated that a deliberate practice (DP) training workshop for responding to ambivalence and resistance resulted in longer‐lasting skill acquisition than the same workshop in a traditional, more didactic format. The present study examined whether this same DP workshop was also efficacious at the level of client motivational language in 4‐month post‐testing interviews used to assess trainee skill in the Westra et al. parent study. Sixty therapists from the community (30 = DP and 30 = traditional) conducted an interview with either an ambivalent simulator or an ambivalent community volunteer. Interviews were coded for interviewee motivational language using the Motivational Interviewing Skills Code (MISC 1.1; Glynn & Moyers, 2009). Counterchange talk (CCT) was further classified into either Ambivalent‐CCT (uttered to disclose conflict about change) or Resistant‐CCT (statements against change uttered to oppose the therapist). Results revealed a significant difference between training groups, with the DP group eliciting less Resistant‐CCT than the traditional training group. This study provides further support for the use of DP training for potentially creating more productive conversations by minimising Resistant‐CCT; a form of speech that has been found to be negatively associated with client outcomes.
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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 | 0.014 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".