Improving pediatric problem-based learning sessions in undergraduate and graduate medical education
Bibliographic record
Abstract
PURPOSE OF REVIEW: Problem-based learning (PBL) sessions have become common alternatives to traditional didactic-style sessions in medical education, including within pediatric education. The creation and execution of PBL sessions, however, can vary among institutions and even between educators at a given institution. Coupling the personal experiences of a recently-graduated medical student with that of a knowledgeable medical educator, the authors sought to analyze two PBL session experiences of the medical student during her second year with the goal of pinpointing specific elements that add value for both learners and facilitators. RECENT FINDINGS: Through this analysis, the authors propose enhancements to PBL sessions that may make them more optimal for developing knowledge in pediatric medicine. These include utilizing an interactive video of the clinical problem to more uniformly assess the learner's knowledge gaps, supporting the creation and evolution of peer-to-peer learning communities, and helping to educate facilitators in how to guide dialogue in this type of educational setting. SUMMARY: The PBL enhancements identified by the authors provide educators with innovative suggestions to better engage pediatric trainees in building social capital, acquiring knowledge, and helping learners retain that knowledge beyond their assessments.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".