Competency-Based Cultural Safety Training in Medical Education at <i>La Sabana</i> University, Colombia: A Roadmap of Curricular Modernization
Bibliographic record
Abstract
Issue: Cultural safety enhances equitable communication between health care providers and cultural groups. Most documented cultural safety training initiatives focus on Indigenous populations from high-income countries, and nursing students, with little research activity reported from low- and middle-income countries. Several cultural safety training initiatives have been described, but a modern competency-based cultural safety curriculum is needed. Evidence: In this article, we present the Competency-Based Education and Entrustable Professional Activities frameworks of the Faculty of Medicine at La Sabana University in Colombia, and illustrate how this informed modernization of medical education. We describe our co-designed cultural safety training learning objectives and summarize how we explored its impact on medical education through mixed-methods research. Finally, we propose five cultural safety intended learning outcomes adapted to the updated curriculum, which is based on the Competency-Based Education model. Implications: This article presents five cultural safety intended learning outcomes for undergraduate medical education. These learning outcomes are based on Competency-Based Education and the Entrustable Professional Activities framework and can be used by faculties of medicine interested in including the cultural safety approach in their curriculum.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".