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Record W4387958502 · doi:10.4324/9781003300465-11

How to Teach Rapport Building Skills to Behavior Analysts

2023· book-chapter· en· W4387958502 on OpenAlexaboutno aff
Ana Luisa Santo, Kimberley Taylor

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldPsychology
TopicBehavioral and Psychological Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyMathematics educationComputer science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.202
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.011

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.

Opus teacher head0.226
GPT teacher head0.375
Teacher spread0.149 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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