Problem-based and related learning approaches in family medicine residency: a scoping review of four countries
Bibliographic record
Abstract
Background: Postgraduate medical education (PGME) bridges the transition from medical school to independent practice. Problem-based learning (PBL), widely used in undergraduate medical education, has emerged as a promising alternative to traditional lectures in PGME. However, its impact on family medicine training remains unclear. Objective: In this scoping review, we describe the use of PBL in family medicine PGME programs and examine its educational and healthcare-related outcomes. Methods: Using Arksey and O'Malley's methodological framework, we conducted a scoping review of PubMed, Embase, PsycINFO, ERIC, Web of Science, and ProQuest in January 2025. Two reviewers independently screened articles, extracting and synthesizing data according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). Results: Twelve studies met inclusion criteria, illustrating diverse PBL delivery methods in family medicine PGME. Programs integrated PBL as standalone sessions, an adjunct, or blended with traditional methods. Learning groups often included mixed specialties (e.g., family medicine and internal medicine) and varied learner levels (e.g., residents and attending physicians). Most studies reported high learner satisfaction and improved perceptions of topics; however, objective assessments of knowledge, pre- and post-PBL, showed no significant improvement. Limited data on behavior and patient outcomes suggested potential benefits. Conclusion: PBL in family medicine PGME appears to enhance engagement and satisfaction but shows mixed educational outcomes. Further research is needed to determine its optimal role in training.
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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 | 0.026 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.025 | 0.024 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| 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".