Gastroenterology Curriculum in the Canadian Medical School System
Bibliographic record
Abstract
Background and Purpose.Gastroenterology is a diverse subspecialty that covers a wide array of topics. The preclinical gastroenterology curriculum is often the only formal training that medical students receive prior to becoming residents. There is no Canadian consensus on learning objectives or instructional methods and a general lack of awareness of curriculum at other institutions. This results in variable background knowledge for residents and lack of guidance for course development.Objectives.(1) Elucidate gastroenterology topics being taught at the preclinical level. (2) Determine instructional methods employed to teach gastroenterology content.Results. A curriculum map of gastroenterology topics was constructed from 10 of the medical schools that responded. Topics often not taught included pediatric GI diseases, surgery and trauma, food allergies/intolerances, and obesity. Gastroenterology was taught primarily by gastroenterologists and surgeons. Didactic and small group teaching was the most employed teaching method.Conclusion.This study is the first step in examining the Canadian gastroenterology curriculum at a preclinical level. The data can be used to inform curriculum development so that topics generally lacking are better incorporated in the curriculum. The study can also be used as a guide for further curriculum design and alignment across the country.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 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) | 0.007 | 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".