When Traditional Model Meets Competencies in Singapore: Beyond Conflict Resolution
Bibliographic record
Abstract
INTRODUCTION: The implementation of competency-based internal medicine (IM) residency programme that focused on the assurance of a set of 6 Accreditation Council for Graduate Medical Education (ACGME) core competencies in Singapore marked a dramatic departure from the traditional process-based curriculum. The transition ignited debates within the local IM community about the relative merits of the traditional versus competency-based models of medical education, as well as the feasibility of locally implementing a training structure that originated from a very different healthcare landscape. At the same time, it provided a setting for a natural experiment on how a rapid integration of 2 different training models could be achieved. MATERIALS AND METHODS: Our department reconciled the conflicts by systematically examining the existing training structure and critically evaluating the 2 educational models to develop a new training curriculum aligned with institutional mission values, national healthcare priorities and ACGME-International (ACGME-I) requirements. RESULTS: Graduate outcomes were conceptualised as competencies that were grouped into 3 broad areas: personal attributes, interaction with practice environment, and integration. These became the blueprint to guide curricular design and achieve alignment between outcomes, learning activities and assessments. The result was a novel competency-based IM residency programme that retained the strengths of the traditional training model and integrated the competencies with institutional values and the unique local practice environment. CONCLUSION: We had learned from this unique experience that when 2 very different models of medical education clashed, the outcome may not be mere conflict resolution but also effective consolidation and transformation.
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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 | 0.019 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.004 |
| 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".