Implementation of problem‐based learning in Asian medical schools and students' perceptions of their experience
Bibliographic record
Abstract
BACKGROUND: Since the introduction of problem-based learning (PBL) at McMaster University in 1969, many medical schools in the USA, Canada and Europe have included PBL in their curricula. In the past decade, many medical schools in Asia have also done so. However, so far no one has questioned whether the outcomes expected of the learner in a PBL setting are applicable to students from different cultural upbringings. AIM: The aim of this study was to investigate the implementation of PBL in Asian medical schools, their students' perceptions of this new mode of teaching/learning and how the problems that have arisen may be overcome. METHOD: Published reports and conference presentations were gathered on the implementation of PBL in some Asian medical schools and comparisons of the experiences in PBL of Asian and students of other ethnic backgrounds. RESULTS: Most Asian medical schools and their students appear to be positive about adapting to PBL in their curriculum. The positive and negative observations appear to be similar to those experienced in non-Asian medical schools. The problems that arose for students in Asian medical schools in the early stages of implementing PBL appear to have been overcome after a period of adjustment with the help of supportive and open-minded tutors. The reports also suggested that trigger problems should be carefully designed to make them relevant and interesting for the students. CONCLUSION: Strong support from the academic administrators (dean and other staff responsible for implementation of the curriculum) in the introduction of PBL into the curriculum and careful training of both faculty and students appear to be key factors to ensure the successful implementation of PBL in Asian medical schools.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".