Assessing undergraduate medical education through a generalist lens
Bibliographic record
Abstract
PROBLEM BEING ADDRESSED: Medical schools aim to integrate the values of generalism into their undergraduate programs. However, currently no program has been described to measure the degree to which formal curricular materials represent generalist principles. OBJECTIVE OF PROGRAM: To quantify the generalism principles present in undergraduate medical education learning materials and to provide recommendations to enhance generalism content. PROGRAM DESCRIPTION: A review of the literature and accreditation documents was conducted to identify key elements of medical generalism. An evidence-informed tool, the Toronto Generalism Assessment Tool, was developed and applied to the new preclerkship undergraduate cases at the University of Toronto in Ontario. The findings regarding the presence of generalism principles and recommendations to enhance generalism content were provided to case developers. The recommendations were valued and were incorporated into subsequent iterations of the cases. CONCLUSION: This is the first report of a successful evidence-informed program to assess the degree of generalism reflected in undergraduate medical education curricular documents. This program can be used by other institutions wishing to review their curricula through a generalist lens.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".