Context‐bound communication skills training: development of a new method
Bibliographic record
Abstract
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.
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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 | 0.015 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".