The effects of problem-based learning during medical school on physician competency: a systematic review
Bibliographic record
Abstract
BACKGROUND: Systematic reviews on the effects of problem-based learning have been limited to knowledge competency either during medical school or postgraduate training. We conducted a systematic review of evidence of the effects that problem-based learning during medical school had on physician competencies after graduation. METHODS: We searched MEDLINE, EMBASE, CINAHL, PsycINFO, Cochrane Databases, and the tables of contents of 5 major medical education journals from earliest available date through Oct. 31, 2006. We included studies in our review if they met the following criteria: problem-based learning was a teaching method in medical school, physician competencies were assessed after graduation and a control group of graduates of traditional curricula was used. We developed a scoring system to assess the quality of the studies, categorized competencies into 8 thematic dimensions and used a second system to determine the level of evidence for each competency assessed. RESULTS: Our search yielded 102 articles, of which 15 met inclusion criteria after full text review. Only 13 studies entered final systematic analysis because 2 studies reported their findings in 2 articles. According to self-assessments, 8 of 37 competencies had strong evidence in support of problem-based learning. Observed assessments had 7 competencies with strong evidence. In both groups, most of these competencies were in the social and cognitive dimensions. Only 4 competencies had moderate to strong levels of evidence in support of problem-based learning for both self-and observed assessments: coping with uncertainty (strong), appreciation of legal and ethical aspects of health care (strong), communication skills (moderate and strong respectively) and self-directed continuing learning (moderate). INTERPRETATION: Problem-based learning during medical school has positive effects on physician competency after graduation, mainly in social and cognitive dimensions.
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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 | 0.024 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".