Status of Medical Education Reform at Saga Medical School 5 Years After Introducing PBL
Bibliographic record
Abstract
In Japan, problem-based learning (PBL) is a relatively new method of educating medical students that is reforming the face of medical education throughout the world, including Asia. It shifts from teacher-centered learning strategies (for example, lectures in large auditoriums) to student-centered, self-directed learning methods (for example, active discussions and problem-solving by students in small groups under the guidance of faculty tutors). Upon a recommendation by the Japan Model Core Curriculum, Saga Medical School introduced a PBL curriculum 5 years ago. A full PBL curriculum was adopted from the McMaster model through Hawaii. A description of how PBL was implemented into the 3rd and 4th year (Phase III curriculum) is given. The overall result has been good. Students who experienced PBL had increased scores on the National Medical License Exam, and Saga increased its ranking from 56th to 19th of the 80 medical schools in Japan. A key step was introduction of the educational scaffolding in PBL Step 0. Students were allowed to see page one of the PBL case, containing the chief complaint, on the weekend before meeting in small groups. Despite a perceived overall benefit to student learning, symptoms of superficial discussions by students have been observed recently. How this may be caused by poor case design is discussed. Other problems, including "silent tutors" and increased faculty workload, are discussed. It is concluded that after 5 years, Saga's implementation of a PBL curriculum has been successful. However, many additional issues, including motivation of students and preparation for PBL in the first 2 years, must still be resolved in the future. This is the first description of the positive and negative outcomes associated with the reform of medical education and the introduction of PBL to a traditional medical school curriculum in Japan.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".