Assessing communication quality of consultations in primary care: initial reliability of the Global Consultation Rating Scale, based on the Calgary-Cambridge Guide to the Medical Interview
Bibliographic record
Abstract
OBJECTIVES: To investigate initial reliability of the Global Consultation Rating Scale (GCRS: an instrument to assess the effectiveness of communication across an entire doctor-patient consultation, based on the Calgary-Cambridge guide to the medical interview), in simulated patient consultations. DESIGN: Multiple ratings of simulated general practitioner (GP)-patient consultations by trained GP evaluators. SETTING: UK primary care. PARTICIPANTS: 21 GPs and six trained GP evaluators. OUTCOME MEASURES: GCRS score. METHODS: 6 GP raters used GCRS to rate randomly assigned video recordings of GP consultations with simulated patients. Each of the 42 consultations was rated separately by four raters. We considered whether a fixed difference between scores had the same meaning at all levels of performance. We then examined the reliability of GCRS using mixed linear regression models. We augmented our regression model to also examine whether there were systematic biases between the scores given by different raters and to look for possible order effects. RESULTS: Assessing the communication quality of individual consultations, GCRS achieved a reliability of 0.73 (95% CI 0.44 to 0.79) for two raters, 0.80 (0.54 to 0.85) for three and 0.85 (0.61 to 0.88) for four. We found an average difference of 1.65 (on a 0-10 scale) in the scores given by the least and most generous raters: adjusting for this evaluator bias increased reliability to 0.78 (0.53 to 0.83) for two raters; 0.85 (0.63 to 0.88) for three and 0.88 (0.69 to 0.91) for four. There were considerable order effects, with later consultations (after 15-20 ratings) receiving, on average, scores more than one point higher on a 0-10 scale. CONCLUSIONS: GCRS shows good reliability with three raters assessing each consultation. We are currently developing the scale further by assessing a large sample of real-world consultations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".