A Comparison of Performance Evaluations of Students on Longitudinal Integrated Clerkships and Rotation-Based Clerkships
Bibliographic record
Abstract
BACKGROUND: Longitudinal integrated clerkship (LIC) students typically perform as well as, if not better than, rotation-based clerkship (RBC) students on objective evaluations, yet few studies have compared performance in the clinical setting. This study compared in-training evaluation report (ITER) ratings of LIC and RBC students, including their correlation with more objective evaluations. METHOD: On the basis of prior academic performance, LIC students (n = 27) at Universities of Alberta, British Columbia, and Calgary were matched with four RBC students from their center. The authors compared reliability of ITER ratings, ITER ratings of clinical skills and professional attributes, and the correlation between ITER ratings and objective evaluations of clinical skills and professional attributes on the Medical Council of Canada Qualifying Examination (MCCQE) Part I. RESULTS: ITER ratings of LIC students were more reliable and significantly higher than those of RBC students for both clinical skills and professional attributes. However, LIC students had lower objective structured clinical examination scores and weaker correlations between subjective and objective evaluations of clinical skills. By comparison, LIC students scored higher on a particular component of the MCCQE and had stronger correlations between subjective and objective evaluations of professionalism. CONCLUSIONS: The discrepancy between ratings of LIC students' clinical skills on ITER and other evaluation formats may be due to differences between the content of training and objective evaluations, or systematic rater biases. Further studies are needed to confirm and explain these findings. Promisingly, our data suggest that the LIC model may allow for a more predictive evaluation of professional competencies.
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".