Problem-based learning in continuing medical education: review of randomized controlled trials.
Bibliographic record
Abstract
OBJECTIVE: To investigate the effects of problem-based learning (PBL) in continuing medical education. DATA SOURCES: PubMed, MEDLINE, EMBASE, CINAHL, and ERIC databases were searched for randomized controlled trials published in English from January 2001 to May 2011 using key words problem-based learning, practice-based, self-directed, learner-centered, and active learning, combined with continuing medical education, continuing professional development, post professional, postgraduate, and adult learning. STUDY SELECTION: Randomized controlled trials that described the effects of PBL on knowledge enhancement, performance improvement, participants' satisfaction, or patients' health outcomes were selected for analysis. SYNTHESIS: Fifteen studies were included in this review: 4 involved postgraduate trainee doctors, 10 involved practising physicians, and 1 had both groups. Online learning was used in 7 studies. Among postgraduate trainees PBL showed no significant differences in knowledge gain compared with lectures or non-case-based learning. In continuing education, PBL showed no significant difference in knowledge gain when compared with other methods. Several studies did not provide an educational intervention for the control group. Physician performance improvement showed an upward trend in groups participating in PBL, but no significant differences were noted in health outcomes. CONCLUSION: Online PBL is a useful method of delivering continuing medical education. There is limited evidence that PBL in continuing education would enhance physicians' performance or improve health outcomes.
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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 | 0.022 | 0.089 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.007 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".