Country report: medical education in France
Bibliographic record
Abstract
CONTEXT: The last 10 years have represented a period of significant reform within both the health care and education systems in France. In terms of its workforce, France faces a shortage of doctors, particularly in primary care. METHODS: This paper examines the French medical curriculum, student selection, licensure and continuing medical education and discusses the challenges currently facing French medical faculties. RESULTS: The French medical curriculum is defined nationally, with methods adapted at medical school level. There has been some uptake of innovative methods such as problem-based learning, skills-based teaching and performance-based assessment. However, traditional didactic teaching of scientific medicine and the apprenticeship model remain dominant. France uses a unique method of selection, which is the subject of much debate. Following a general year, medical students are subject to a selection examination that permits only a small number to continue studies. Similarly, at the end of medical school, a written test is used to rank students for the purpose of matching to specialty training. France has no national colleges or licensing authorities and thus authorisation to practise rests on the diploma delivered by each faculty of medicine. From 2005, continuing medical education became compulsory for all doctors. It includes the evaluation of medical practice. CONCLUSIONS: French faculties of medicine face several challenges, including: rising numbers of students without a commensurate growth in the number of faculty members; an increasing emphasis on multidisciplinary health care; a drive towards mandatory continuing education and performance-based outcomes, and the development of national selection examinations that are knowledge-based.
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 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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".