Evaluating the outcomes of problem-based learning in postgraduate medical education: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Educators have recently been compelled to incorporate more active instructional formats into medical education, such as problem-based learning (PBL). In view of the mixed outcome data on the use of PBL in postgraduate medical education (PGME), there is a need to synthesize the data to inform the application of PBL in PGME contexts. Objective: The aim of this systematic review and meta-analysis was to synthesize learning outcomes of PBL in PGME contexts. Methods: The authors systematically searched MEDLINE, Embase, APA PsycINFO, AMED, CINAHL, Web of Science, ERIC, and Cochrane databases from January 1, 1950, to July 1, 2022 for original studies that reported Kirkpatrick outcomes of PBL in PGME contexts. Outcomes data were extracted. Quantitative data relating to learning outcomes were meta-analyzed using a random-effects model to generate weighted mean differences. Results: Of 4310 abstracts screened, the authors included 21 studies encompassing anesthesia, family medicine, internal medicine, occupational medicine, pediatrics, psychiatry, public health and surgical residency programs. The studies reported reaction (n = 12), learning (n = 15), behavioural (n = 6) and/or results outcomes (n = 4). Meta-analysis of the three eligible articles demonstrated no significant difference after PBL in pre- and post-test results (pooled mean difference=0.13%, 95% CI, -6.74–7.00). There were observed improvements in satisfaction levels and self-reported behavioural outcomes following PBL. Conclusions: Although similar learning outcomes were observed using PBL and the usual teaching in PGME, PBL was associated with benefits in trainee satisfaction and behavioural changes that contribute to learning and performance. PGME programs should consider incorporating PBL into curricula.
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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 | 0.036 | 0.085 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.048 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".