Team-based assessment of medical students in a clinical clerkship is feasible and acceptable
Bibliographic record
Abstract
BACKGROUND: This study describes the development, implementation and evaluation of a team-based, multi-source method of assessment in which students on a clinical clerkship were provided with feedback on their performance as observed by physicians, residents, nurses, peers, patients and administrators. METHODS: The instrument was developed by reviewing existing assessment items and by obtaining input from assessors and students. Numerical data and written comments provided to students were collected, internal consistency was estimated and interviews and focus groups were used to determine acceptability to assessors and students. RESULTS: A total of 1068 assessors completed 3501 forms for 127 students. Internal consistency estimates for each assessment form were acceptable (Cronbach's alpha 0.856-0.948). Each student received an average of 188 words of written feedback divided into an average of 26 'Areas of Excellence' and 5 'Areas for Improvement'. Interviews revealed that the majority of students and assessors interviewed found the method acceptable. CONCLUSIONS: This study demonstrates that a team-based model of assessment based on the principles of multi-source feedback is a feasible and acceptable form of assessment for medical students learning in a clinical clerkship, and has some advantages over traditional preceptor-based assessment. Further studies will focus on the strengths and weaknesses of this novel assessment technique.
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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 | 0.016 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".