Problem-based learning in continuing medical education: review of randomized controlled trials.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.132 | 0.229 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it