Patients' global ratings of student competence. Unreliable contamination or gold standard?
Bibliographic record
Abstract
PURPOSE: To determine whether global ratings by patients are valid and reliable enough to be used within a major summative assessment of medical students' clinical skills. METHOD: In 11 stations of an 18-station objective structured clinical examination (OSCE), where a student was asked to educate or take a history from a patient, the patient was asked, 'How likely would you be to come back and discuss your concerns with this student again?' These 11 opinions were aggregated into a single patient opinion mark and correlated with other measures of student competence. The patients were not experienced in student assessment. RESULTS: A total of 204 students undertook the OSCE. Reliability of patient opinion across all 11 stations revealed a Cronbach alpha of 0.65. The correlation coefficient between the patient ratings and the total OSCE score was good (r = 0.74; P < 0.001) and was better than the correlation between any single OSCE station and the total OSCE score. It was also better than the correlation between the aggregated patient opinion and tests of student knowledge (r = 0.47). CONCLUSION: It is known that patients can reliably complete checklists of clinical skills and that doctors can reliably provide global ratings of students. We have now shown that, by controlling the context, asking the right question and aggregating several opinions, untrained patients can provide a reliable and valid global opinion that contributes to the assessment of a student's clinical skills.
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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 | 0.020 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".