The impact of a deliberate practice workshop on therapist demand and support behavior with community volunteers and simulators.
Bibliographic record
Abstract
The present study used a newly developed simplified coding system, the Therapist Demand and Support Code, to examine specific therapist behaviors in the context of a previously conducted training trial on Deliberate Practice (DP). The parent trial randomized trainees to a DP workshop or its Traditional, more didactic counterpart (Westra et al., 2020). In both groups, trainees were taught to use Support, rather than Demand, for managing ambivalence and resistance, with the DP group having more feedback and practice. In this study, 68 trainees interviewed both an ambivalent community volunteer and an ambivalent simulator 4 month post workshop. The DP group was found to exhibit significantly fewer Demand behaviors than the Traditional group, with the latter also being significantly quicker to use Demand in the interviews. Moreover, the simulator evoked significantly greater Demand from therapists, regardless of the Training group, suggesting the simulators were more resistant. Although therapist use of Support was equal for community volunteers across training groups, Traditional workshop trainees decreased Support when interviewing the more resistant simulators, whereas DP trainees increased their Support with this same group. This is consistent with findings that DP trainees were more appropriately responsive, making fewer Demands following interviewee counterchange talk and using more Support at these times. These results provide some initial validation of the simplified therapist behavior coding system and offer further evidence for the benefits of DP workshop training for managing resistance. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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 | 0.013 | 0.045 |
| 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 | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".