Problem-Based Learning in North American Primary Care Postgraduate Medical Education: A Rapid Review
Bibliographic record
Abstract
Problem-based learning (PBL) in medical education is centered around a problem or case and is learner-led, involving small groups and problem-solving. PBL is ubiquitous in North American undergraduate medical education (UGME) due to reported learner satisfaction, efficacy, and long-term knowledge retention; however, its application to postgraduate medical education (PGME) is less defined. This review addresses the knowledge gap on the use and efficacy of PBL in PGME, specifically among primary care specialties due to their unique training needs, using the Kirkpatrick model as the theoretical basis for interpreting results. A search for articles using PubMed resulted in 17 selected articles that included primary care PGME learners undergoing at least one PBL session led by another learner. Learners were overwhelmingly satisfied with PBL, reporting increased confidence and comfort in the subject area. While none of the studies measured behavior change objectively, over half reported increased comfort in diagnosing, prescribing, and managing patients. This review extends the positive feedback found from PBL in UGME settings to apply to PGME and highlights the suitability of PBL for primary care due to increased confidence, learner satisfaction, perceived knowledge gain, and objective learning 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.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".