Scope of coverage of medical genetics and genomics in pre‐clerkship programs of Canadian faculties of medicine: A curriculum analysis
Bibliographic record
Abstract
We appraised the scope of medical genetics and genomics concepts covered in the pre-clerkship programs of Canadian faculties of medicine through an analysis of course objectives. All course objectives linked to medical genetics and genomics in pre-clerkship programs of Canadian faculties of medicine were compiled. From this, the fraction of objectives dedicated to medical genetics and genomics was calculated. Course objectives were also categorized according to a curriculum and a competency classification. Of the 17 Canadian faculties of medicine, the complete set of course syllabi (5 faculties) or the listing of learning objectives (4 faculties) were obtained and reviewed. The fraction of learning objectives dedicated to medical genetics and genomics varied between 0.65% and 5.05%. From the objectives classification, "foundational knowledge" was most frequently covered (64% of the compiled objectives), while topics such as: "ethics and professionalism," "communicate genetics information," and "obtain specialist help" were covered by less than 5%. Coverage of medical genetics and genomics in pre-clerkship programs of Canadian faculties of medicine appears to be low. Genetics and genomics are playing a rapidly expanding role in healthcare and clinical practice and educational programs should consider this new reality.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".