The Development of Outcome-Based Curriculum in Medical Schools Outside Korea
Bibliographic record
Abstract
In medicine, rapid changes in information, technology, socio-economic interests, and globalization affect the medical education focused on the competencies of doctors, and the number of medical schools that are adopting an outcome-based curriculum (OBC) is increasing worldwide. This paper introduces the OBC model of 5 trailblazing medical schools from the UK, US, and Australia, comparing their unique features, followed by brief comment about Canada and the EU as well. On developing an OBC, the process of establishing the top outcomes for graduates is similar and the outcomes comprise knowledge, skills, and attitudes about science, patients, colleagues, society, and themselves. Implementing the outcomes down into the sub-levels of the curriculum is much more complicated and time-consuming. Assessing the achievement of every outcome is essential and requires the use of many tools in addition to the traditional written examination. From the perspective of adult learning theory, self-directed learning, team-learning, and individual and flexible achievement are tested and executed in an OBC. The gradual expansion and further innovation of an OBC is expected so that tomorrow’s doctors will be able to meet the challenges of the future.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".