A Framework for a Competency Based Medical Curriculum in Saudi Arabia
Bibliographic record
Abstract
BACKGROUND: We recently adopted a competency based curriculum based on the CanMEDs model. This shift required the cross-mapping of all key CanMEDs competencies with the competencies for higher education in Saudi Arabia as per the Saudi National Commission for Academic Accreditation & Assessment (NCAAA) guidelines. OBJECTIVES: To formulate competencies for our curriculum and to create a framework aligned with NCAAA, CanMEDs and Saudi Meds. METHODS: After finalization of program outcomes, the program goals were cross-mapped with CanMEDs and Saudi Meds competencies and then the CanMEDs competencies were reverse mapped with our outcomes. Finally benchmarking of outcomes with the programs of the Universities of Manitoba and Toronto was done. RESULTS: We were able to cross-map and match major outcomes of our program with both the CanMEDs and the Saudi Meds frameworks, ensuring that the outcomes are in line with NCAAA, CanMEDs and Saud Meds. Also, our program objectives were bench marked with two of the Canadian medical schools. CONCLUSION: We propose that our framework can be a model for other universities in Saudi Arabia to consider when shifting to a competency based 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 | 0.013 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".