Medical education in Brazil
Bibliographic record
Abstract
This paper aims to describe and analyze medical education in Brazil, a history of over 200 years. As in most European countries and influenced by the Flexner Report, an undergraduate medical course in Brazil takes 6 years. Recently, medical education research has been advocating a shift from a teacher-centered and hospital-based approach to student-centered and community-based education. Nevertheless, a huge variation exists among Brazilian medical schools. The physicians' supply program known as "More Physicians" has strongly impacted the number of medical schools in Brazil, which is growing rapidly. Professors of medicine from several institutions and other stakeholders have alerted authorities to the risks of operating so many schools without adequate time to prepare teachers, clinician-educators, curricula, and sufficient pedagogical structure to ensure quality medical education. The possibility of an imminent catastrophe in medical education has united stakeholders in pursuit of a guarantee of quality maintenance. This effort has resulted in the creation of an independent accreditation system approved by the World Federation of Medical Education. The study of the unbalanced relationship between stakeholders in medical education in Brazil until now has provided valuable information concerning the importance of having their roles and limits clear. It is possible that these findings might be replicable around the world.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".