XXI–XXVI QUARTERLY Review Problem-based learning �
Bibliographic record
Abstract
Problem-based learning has been used in medical school in a number of different countries around the world for over 50 years, with both undergraduate and graduate students. Instead of the traditional lectures, laboratory practical classes and tutorial system of education, students in small groups are presented with a problem that they must try to solve. They are assisted by a ‘facilitator ’ who helps them formulate the problem and generally advises them but does not supply information. The students have to decide what information they need to solve the problem, find it and communicate it to the others in the group. At this stage a solution may be apparent, but several more group discussions to reformulate the problem followed by re-iterations of the information seeking process may be needed before a solution can be found. The theory is that because information is sought and presented in a relevant context, it is valued and is more likely to be remembered. At the end of the session student reflect on how they performed. Problem-based learning has been criticised from a number of points of view, especially that it does not present a coherent curriculum, the curriculum is not necessarily ‘covered’, and that in many medical schools the implementation has been less than optimal. For over 50 years problem-based learning (PBL) has been a method of education, mainly in medical schools in Canada and U.S.A (Boud & Feletti, 1997). Instead of following a set curriculum with lectures and other classes, students are presented with a problem and work in small groups with a ‘facilitator’. They try to formulate the problem in terms they can understand, decide what information they need to solve it, find the infor-� th
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.052 | 0.025 |
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".