Formación científica en el pregrado de medicina en Chile: ¿dónde estamos? y ¿hacia dónde vamos?
Bibliographic record
Abstract
For more than a century the training of medical professionals has been organized according to the Flexnerian model, which comprises three cycles: basic, clinical and clerkship. On the other hand, the accelerated development of biomedical sciences modified the competences of the first cycle. Additionally, new skills required for medical practice, such as teamwork and innovation as a tool to solve health problems, challenged in recent years the classic paradigm of medical education. Therefore, the medical schools have developed multiple strategies to deal with it, such as curricular integration using competency-based education models, incorporating basic and clinical sciences in parallel during the curriculum, ensuring a relevant and applicable scientific knowledge throughout the training process. Although in Chile the Flexner prototype is still followed, the basic sciences are taught as single or integrated courses or using a systems approach. In this article we report a diagnosis about the local integration of fundamental sciences in medical training. We also compare our schools with those of Canada, Europe and Latin America. Recommendations aimed at modernizing medical school curricula are made.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".