Longterm effects of problem‐based learning: a comparison of competencies acquired by graduates of a problem‐based and a conventional medical school
Bibliographic record
Abstract
BACKGROUND: Problem-based learning (PBL) as an approach to the instruction of medical students has attracted much attention in recent years. However, its effect on the performance of its graduates is the subject of considerable debate. This article presents data from a large-scale study among graduates of a problem-based medical school and those of a conventional medical school to contribute to this discussion. PURPOSE: To study the longterm effects of problem-based medical training on the professional competencies of graduates. METHODS: A questionnaire was sent to all graduates since 1980 of a problem-based and a conventional medical school. Participants were requested to rate themselves on 18 professional competencies derived from the literature. RESULTS: The graduates of the PBL school scored higher on 14 of 18 professional competencies. Graduates of the problem-based school rated themselves as having much better interpersonal skills, better competencies in problem solving, self-directed learning and information gathering, and somewhat better task-supporting skills, such as the ability to work and plan efficiently. There were no sizeable differences with regard to general academic competencies, such as conducting research or writing a paper. Graduates from the conventional school rated themselves as having slightly more medical knowledge. The findings were shown to be valid and robust against possible response bias. CONCLUSION: The findings suggest that PBL not only affects the typical PBL-related competencies in the interpersonal and cognitive domains, but also the more general work-related skills that are deemed important for success in professional practice.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".