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Context‐bound communication skills training: development of a new method

2002· article· en· W2078921064 on OpenAlexaff
Stephen Rollnick, Paul Kinnersley, Christopher Butler

Bibliographic record

VenueMedical Education · 2002
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsMcMaster University
FundersOffice of Research and Development
KeywordsContext (archaeology)Communication skillsMedical educationCommunication skills trainingMedicineSimulated patientTraining (meteorology)Work (physics)Skills managementNursingPsychology

Abstract

fetched live from OpenAlex

OBJECTIVES: To examine how communication skills training might be integrated into everyday clinical practice in a manner that is acceptable to clinicians. DESIGN: General practitioners from 3 group practices agreed to take part, in turn, in a study of how to manage difficult consultations about antibiotic prescribing for acute respiratory infections. This provided the opportunity to conduct communication skills training in which lessons learned from one practice were taken into the next. SETTING: United Kingdom general practices. SUBJECTS: Three groups of general practitioners. FINDINGS: Difficulties with the acceptability of a traditional off-site workshop approach, using role play as the main teaching method, led to the development of a new training method (context-bound training), which proved to be practical and acceptable to experienced clinicians. The main features of the method were the delivery of training in the clinicians' place of work, and the transformation of their reported difficult cases into scenarios which they then encountered with a standardized simulated patient before and after brief seminars. Everyday clinical experience was kept in the foreground and 'communication skills' in the background. CONCLUSIONS: The method is acceptable to clinicians and adaptable to a range of clinical situations. It offers potential for improving the communication skills of clinicians both in hospital and primary care settings.

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)0.001
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.829
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.299
GPT teacher head0.516
Teacher spread0.217 · 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; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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

Citations104
Published2002
Admission routes1
Has abstractyes

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