Problem-based learning and the medical school: another case of the emperor’s new clothes?
Bibliographic record
Abstract
For almost four decades, problem-based learning (PBL) has been the stated cornerstone of learning in many medical schools. Proponents of PBL cite the open nature of the learning experience where students are free to study in depth, unencumbered by the burdens of broad courses based on the memorization of facts; detractors, on the other hand, cite the lack of breadth and factual knowledge required for professional qualification. Both points of view have merit. Professional schools have a different set of needs and requirements, and it is these that drive the curriculum and learning philosophies. The constraints of the professional school are so different from those of the purely academic environment that PBL, while admirably suited to the latter, is just problem solving in the former.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.051 |
| Scholarly communication | 0.011 | 0.021 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.013 | 0.024 |
| Insufficient payload (model declined to judge) | 0.011 | 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".