Problem-based learning in undergraduate medical education: can we really implement it in the West African subregion?
Bibliographic record
Abstract
BACKGROUND: A major global pedagogical shift has occurred in the way medicine is taught over the last half a century. Problem-based learning (PBL) has emerged as one of the most popular, of these learner-centred new methods. OBJECTIVE: To examine the evolution and educational principles of PBL and the feasibility of implementing it in the West African subregion. METHODS: Key literature detailing the history, educational value and principle behind PBL were reviewed. Issues regarding the implication of implementing PBL to West Africa were deduced and suggestions made for the way forward. RESULTS: Since its introduction in McMaster University in Canada in the 60s, PBL has spread all over world. It is rooted in sound educational theories like the Kolb's experiential learning, adult education, collaborative learning, contextual learning and constructivism. Compared to traditionally trained students, PBL students find learning more enjoyable and develop better relational and professional skills. They show more causal reasoning in diagnosis and become better lifelong learners. Issues that may affect its implementation in West Africa include high start up costs, lack of supporting educational technology and relative lack of medical school managers with appropriate medical education background to assess and evaluate such innovations. CONCLUSION: The evidence for the need for a change from the traditional method of training is overwhelming. Implementation of PBL as an educational method in medical training in the West African subregion is both desirable and practicable if we address some of the issues outlined above.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".