Competency-based anesthesiology teaching: comparison of programs in Brazil, Canada and the United States
Bibliographic record
Abstract
In 2017, the Brazilian Society of Anesthesiology (SBA) and the National Medical Residency Committee (CNRM) presented a joint competence matrix to train and evaluate physicians specializing in Anesthesiology, which was enforced in 2019. The competency-based curriculum aims to train residents in relation to certain results, in that residents are considered capable when they are able to act in an appropriate and effective manner within certain standards of performance. Canada and the United States (US) also use competency-based curriculum to train their professionals. In Canada, the format is the basis for using an evaluation method known as Entrustable Professional Activities (EPA), in which the mentor assesses residents' capacity to perform certain tasks, classified in 5 levels. The US, in turn, uses Milestones as evaluation, in which competencies and sub-competencies are assessed according to residents' progress during training. The present article aims to describe and compare the different competency-based curriculum and the evaluation methods used in the three countries, and proposes a reflection on future paths for medical education in Anesthesiology in Brazil.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".