Clerkship evaluation–what are we measuring?
Bibliographic record
Abstract
BACKGROUND: As society's expectations of physicians change, so must the objectives of training. Professional organizations involved in training now emphasize multiplicity of roles. But how well do we evaluate these multiple roles? AIMS: To investigate the principal components of evaluation in the Internal Medicine clerkship rotation at the University of Calgary. METHODS: We performed factor analysis on all evaluation components in the Internal Medicine clerkship rotation, including the in-training evaluation report (ITER), objective structured clinical examination (OSCE), and multiple choice questions (MCQ) examination. RESULTS: We identified three principal components: information processing, professionalism, and declarative knowledge. Both the OSCE and MCQ loaded on a single factor, declarative knowledge. The nine items on the ITER loaded on two factors-information processing and professionalism. CONCLUSIONS: Despite using 11 evaluation items on three tools, we identified only three principal components of evaluation. Both our MCQ and OSCE appeared to measure declarative knowledge. The latter may be due to the fact that we use standardized patients without clinical findings-such that evaluations are primarily based upon the demonstration of examination routines. Reasons for the lack of discriminant validity of our ITER include overlapping attributes and constant errors, including a halo effect and an error of leniency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.160 | 0.346 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| 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".