Challenges Facing the Shift from the Conventional to Problem-Based Learning Curriculum
Bibliographic record
Abstract
Tremendous changes have taken place in medical curricula in the last two decades; these changes have arguably created some imbalances in the quality of medical graduates around the globe, which may be partly due to the number of resources often demanded by the design of the newer curricula. Therefore, resource-poor countries are often unable to adopt these newer models of training in their entirety and are thus compelled to follow the so- called “Subject-Based Curriculum”. The authors have discussed and prepared some guidelines to provide direction for the adaptation and implementation of Problem-Based Learning Curriculum (PBLC) in countries with different cultures and limited resources. This article addresses the issues and concerns raised by medical educationists on the implementation of PBLC especially in developing countries. These pointers include practical solutions for such common problems as staff, cost, infrastructure and training.
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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 | 0.035 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".