Competencies and entrustable professional activities: new models for elaboration of a curriculum framework for family and community medicine residency
Bibliographic record
Abstract
Objective: The purpose is to carry out a literature review on Competency Based Curriculum that could support the elaboration of a matrix for the Residency Program in Family and Community Medicine of Fortaleza, Ceará. Methods: A literature review was made on the theoretical reference of competency-based education, selecting articles, guidelines, documents and curricula models of medical schools and national and international entities involved with medical education. Results: The literature review evidenced two main curriculum models repeatedly mentioned in the references: ACGME (Accreditation Council for Graduate Medical Education) Milestones and CanMEDS (Canadian Medical Education Directions for Specialists) Framework. Competency-Based Curriculum emphasizes student-centered teaching and uses a results-based approach to the design, implementation, and evaluation of medical education programs. It is organized as a framework of competencies mapped to entrustable professional activities in the form of a matrix. The evaluation is based on performance through milestones. For creating its own curricular Matrix, the CanMEDS 2015 was adopted as a model because it was approved by 12 Canadian medical organizations and it is currently used as reference in dozens of countries, being the most widely applied model in the world. Conclusion: We expect that this review would serve as a tool for other Medical Schools and their Residency Programs to develop their own Competency-Based Curricula.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".