“Who writes what?” Using written comments in team-based assessment to better understand medical student performance: a mixed-methods study
Bibliographic record
Abstract
BACKGROUND: Observation of the performance of medical students in the clinical environment is a key part of assessment and learning. To date, few authors have examined written comments provided to students and considered what aspects of observed performance they represent. The aim of this study was to examine the quantity and quality of written comments provided to medical students by different assessors using a team-based model of assessment, and to determine the aspects of medical student performance on which different assessors provide comments. METHODS: Medical students on a 7-week General Surgery & Anesthesiology clerkship received written comments on 'Areas of Excellence' and 'Areas for Improvement' from physicians, residents, nurses, patients, peers and administrators. Mixed-methods were used to analyze the quality and quantity of comments provided and to generate a conceptual framework of observed student performance. RESULTS: 1,068 assessors and 127 peers provided 2,988 written comments for 127 students, a median of 188 words per student divided into 26 "Areas of Excellence" and 5 "Areas for Improvement". Physicians provided the most comments (918), followed by patients (692) and peers (586); administrators provided the fewest (91). The conceptual framework generated contained four major domains: 'Student as Physician-in-Training', 'Student as Learner', 'Student as Team Member', and 'Student as Person.' CONCLUSIONS: A wide range of observed medical student performance is recorded in written comments provided by members of the surgical healthcare team. Different groups of assessors provide comments on different aspects of student performance, suggesting that comments provided from a single viewpoint may potentially under-represent or overlook some areas of student performance. We hope that the framework presented here can serve as a basis to better understand what medical students do every day, and how they are perceived by those with whom they work.
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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 | 0.009 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".