How to Teach Rapport Building Skills to Behavior Analysts
Bibliographic record
Abstract
Behavioral clinicians have historically not been taught how to build and maintain rapport with clients or mediators throughout their educational careers. Research from other health disciplines has shown that increased therapeutic rapport between clinician and client leads to better treatment outcomes. A comprehensive two-day workshop was developed, using a Behavioral Skills Training approach, to teach a set of rapport-building behaviors to behavioral clinicians. Through targeted skills in the areas of active listening, communication, empathy, and compassion the goal was to improve both fidelity and adherence to behavioral interventions as well as client and mediator satisfaction. The workshop is guided by theory from Social Work and Counseling fields and includes modules on Anti-Oppressive Practice and ethics. It also introduces several tools that clinicians can use to elicit opportunities for rapport-building with client and mediators and to track fidelity and adherence over time. The workshop was implemented across several groups of behavioral clinicians within a social services agency in Toronto. Preliminary results showed a marked increase in rapport-building behaviors from pre-test to post-test, and participant feedback revealed a high level of social validity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.014 |
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".