Web-based evaluation of medical clerkships: a new approach to immediacy and efficacy of feedback and assessment
Bibliographic record
Abstract
The classical assessment of medical students has been based on the 'apprenticeship' model, which consists of written examinations and tutor-led assessment of coursework; this method has proved to be very dependent on tutors' attitudes and instructional skills and on patients' availability. This paradigm has shifted in recent years as a result of the new implementation of modular assessments, logbooks and portfolios during clerkship rotations. Portfolios have been demonstrated to bea very useful tool to promote self-reflection, prompt feedback and skills development. However, written portfolios not only introduce additional paperwork for both students and tutors but also have some limitations for immediacy and effectiveness of feedback. To obviate these limitations an electronic portfolio is proposed here to be used by students and tutors during clinical clerkships. Based on the principles for good practice in undergraduate education the authors show the advantages of this method and its multiple applications to promote students' development of skills and attitudes and to improve tutors' acceptance of this innovative evaluation method.
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 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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.005 | 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".