Is Problem-Based Learning a Quality Approach to Education in Health Sciences?
Bibliographic record
Abstract
The Faculty of Health Sciences at McMaster University has pioneered, experimented and finally excelled in the application of problem-based learning (PBL) as an entire medical curriculum for the past 35 years. However, the general practice of PBL by other medical schools around the globe has progressed slowly. In theory, PBL as an educational philosophy has long been considered as a quality cognitive concept and was adopted by many medical schools via curriculum reform to improve students' learning attitude. In practice, what is the experimental evidence for PBL meeting the expectation of a quality education in health sciences? How do we differentiate problems associated with PBL philosophy per se from those associated with the ways PBL are handled and implemented? I will address these questions from the perspective of the assessment of performance of students, graduates and practising physicians from the PBL track compared to those from the conventional track based on literature information. Ample evidence suggests that PBL is superior in producing more compassionate physicians and graduates with lifelong learning and leadership quality. But, some educators and administrators are still skeptical that the benefits from PBL may be too marginal to justify the resources required in sustaining it. In this presentation, the assessment of PBL, in both theoretical and practical terms, will be discussed using McMaster PBL as a convenient example because of its relatively long history in practising PBL in medical education.
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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 | 0.021 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| 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".