Educating doctors in France and Canada: are the differences based on evidence or history?
Bibliographic record
Abstract
BACKGROUND: Despite many economic and political similarities between France and Canada, particularly in their health care systems, there are very significant differences in their systems of medical education. AIM: This work aims to highlight the sociohistorical values of each country that explain these differences by comparing the medical education systems of the 2 countries, including medical schools (teachers, funding), key processes (curriculum, student selection) and quality assurance methods. DISCUSSION: In France, means and processes are standardised and defined at a national level. France has almost no national system of assessment of medical schools nor of students. By contrast, Canada leaves medical schools free to design their medical curricula, select students and appoint teachers using their own criteria. In order to guarantee the homogeneity and quality of graduates, the medical profession in Canada has created independent national organisations that are responsible for accreditation and certification processes. Each country has a set of founding values that partly explain the choices that have been made. In France these include equality and the right to receive free education. In Canada, these include equity, affirmative action and market-driven tuition. CONCLUSION: Many of the differences are more easily explained by history and national values than by a robust base of evidence. There is a constant tension between a vision of education promoted by medical educators, based on contextually non-specific ideas such as those found in the medical education literature, and the sociopolitical foundations and forces that are unique to each country. If we fail to consider such variables, we are likely to encounter significant resistance when implementing reforms.
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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 | 0.001 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".