Consultation skills of last semester medical students in Sweden: video-recorded real-patient consultations in primary health care assessed by Calgary-Cambridge Global Consultation Rating Scale, a pilot study
Bibliographic record
Abstract
This article was migrated. The article was marked as recommended. Introduction: Doctor-patient consultation is an essential element of high quality health care. Education and training of medical students in consultation skills is important. The aim of this study was to investigate the medical students' consultation skills before graduation by assessment of the students' video recordings of consultations with real patients at primary health care centres. Methods: All students had to make a video recording of a meeting with a real patient for formative examination. 26 students participated in the study and delivered a video recording and a self-assessment. Four general practitioners assessed the video recordings by Calgary-Cambridge Global Consultation Rating Scale (CC-GCRS). Statistical testing included comparisons between groups of students and assessors using non-parametric methods. Results: The average CC-GCRS-rating was higher for female students. The students' strengths were related to relation and problem exploration. Their limitations were related to patient's perspective, providing structure and providing information. The students assessed their consultation skills higher than the assessors did, while the relative levels were similar. The distribution of rating scores across the assessors was small. Conclusion:Consultation skills were acceptable for most medical students, although there was room for improvement regarding patient centeredness skills. CC-GCRS was feasible and might be a valuable instrument to assess consultation skills for medical students at the end of their medical education.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".