Teaching and assessment of explanation and planning in medical schools in the United Kingdom: Cross sectional questionnaire survey
Bibliographic record
Abstract
BACKGROUND: The second half of the consultation (explanation and planning) has been described as neglected in communication skills teaching in medical schools, with programmes concentrating on information gathering. AIMS: To identify the extent and ways in which explanation and planning is taught in medical schools in the United Kingdom. METHODS: Questionnaire mailed to consultation teaching leads. RESULTS: Teaching in explanation and planning occurred in 32/32 medical schools. Teaching commenced in years 1 or 2 in 14/32 and year three onwards in 17/32 medical schools. Teaching took place over one year in 12/32, two years in 13/32, three years in 3/32 and 4 years in 2/32 medical schools. Different teaching methods were used, but most use discussion and rehearsal of skills with a simulated patient (21/32). 20/32 medical schools based their teaching on a published consultation model, with 18/20 using the Calgary Cambridge Guide to the Consultation. 31/32 medical schools examined their students in explanation and planning: formative assessment (3/31) or summative (16/31) or both (9/31). CONCLUSIONS: There is now universal teaching and almost universal assessment of the second half of the consultation in medical schools in the UK. Timing of teaching, educational methods, consultation models and assessment used varies between schools.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".