Implementation of PBL curriculum involving multiple disciplines in undergraduate medical education programme
Bibliographic record
Abstract
This article describes how a multidisciplinary problem-based learning (PBL) curriculum was established at the International Medical University in Malaysia for preclinical education in a 5-semester phase 1 programme. Based on positive feedback from a modified PBL program implemented in one discipline, a multidisciplinary PBL curriculum was established. PBL training for facilitators and students, development of resource materials, curriculum design, and case writing were done in a manner that is consistent with the characteristics and learning style preferences of undergraduate medical students. About 80 percent of the lectures were kept in the new PBL program. The multidisciplinary PBL curriculum has been successful in helping undergraduate medical students mentally construct an understanding of the interrelationship between medical knowledge and basic science concepts. The experience ai IMU indicates that there are clear benefits for students in the PBL format. A benefit to faculty is that PBL tutorial facilitators were partly liberated from their traditional roles and developed additional skills for facilitating. However, conflict arises when PBL-trained students encounter the traditional exam-centered education system.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".