Development and validation of an instrument to assess and improve clinical consultation skills
Bibliographic record
Abstract
<p>Context: Development of medical students’ consultation skills with patients is at the core of the UK General Medical Council’s 'Tomorrow’s Doctors' guide (2009). Teaching and assessment of these skills must therefore be a core component of the medical undergraduate curriculum. The Calgary Cambridge guide to the medical interview and the Leicester Assessment Package (LAP) provide a foundation for teaching and assessment, but both have different strengths. Objective: To develop and validate a comprehensive set of generic consultation competencies. Design: The Calgary Cambridge guide to the medical interview was revised to include ‘clinical reasoning’, ‘management’, ‘record keeping’ and ‘case presentation’. Each section was populated with competencies generated from Tomorrow’s Doctors (2009), the LAP and the Calgary Cambridge guide to the medical interview. A Delphi validation study was conducted with a panel drawn from hospital and general practice clinical tutors from eight UK medical schools. Main outcome measures: A priori consensus standards for inclusion (or exclusion) of an element were: at Stage 1 =70% agreement (or disagreement) that the item should be included; at Stage 2 =50% agreement (or disagreement) that the item should be included. If more than 10% of respondents suggested a thematically similar new item (or rewording of an existing item) in Stage 1, it was included in Stage 2. Results: The design stage resulted in a set of 9 categories of consultation skills with 58 component competencies. In the Delphi study all the competencies reached 70% agreement for inclusion, with 24 suggested amendments, all of which achieved consensus for inclusion at Stage 2. Conclusion: We have developed a Generic Consultation Skills assessment framework (GeCoS) through a rigorous initial development and piloting process and a multi-institutional and multi-speciality Delphi process. GeCoS is now ready for use as a tool for teaching, formative and summative assessment in any simulated or workplace environment in the hospital or community clinical setting.</p><div><br></div>
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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 | 0.062 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".