Should Canadian Medical Schools Implement a Widespread 3 Year Medical Curriculum?
Bibliographic record
Abstract
Background: This paper addresses the potential costs and benefits of implementing a widespread 3 year medical curriculum across the country.Methods: We compared differences in curriculum, costs, workforce production, competency, exposure to experiences, timing of career choices, and maturity of students and physicians between 3 and 4 year programs. We accessed this information from 5 school’s online course outlines and by performing a broad search of the literature.Results- Three and four year medical programs have very similar curricular content. The most significant cost savings in a 3 year medical program are due to these students entering the workforce a year earlier. A 3 year program would add more physicians to our workforce initially; however, more doctors are produced over the long term by expanding class sizes. Test scores of graduates from 3 year programs in Canada and the US are similar to graduates from 4 year medical programs. A shorter program could limit the exposure of students to extra curriculars and force them to make earlier career decisions; however, time spent in electives appears to be similar.Conclusions: We do not find enough compelling evidence to advocate switching all medical schools in Canada to a 3 year medical program.
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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".