A Narrative Review and Novel Framework for Application of Team-Based Learning in Graduate Medical Education
Bibliographic record
Abstract
ABSTRACT Background Team-based learning (TBL) promotes problem solving and teamwork, and has been applied as an instructional method in undergraduate medical education with purported benefits. Although TBL curricula have been implemented for residents, no published systematic reviews or guidelines exist for the development and use of TBL in graduate medical education (GME). Objective To review TBL curricula in GME, identify gaps in the literature, and synthesize a framework to guide the development of TBL curricula at the GME level. Methods We searched PubMed, MEDLINE, and ERIC databases from 1990 to 2014 for relevant articles. References were reviewed to identify additional studies. The inclusion criteria were peer-reviewed publications in English that described TBL curriculum implementation in GME. Data were systematically abstracted and reviewed for consensus. Based on included publications, a 4-element framework—system, residents, significance, and scaffolding—was developed to serve as a step-wise guide to planning a TBL curriculum in GME. Results Nine publications describing 7 unique TBL curricula in residency met inclusion criteria. Outcomes included feasibility, satisfaction, clinical behavior, teamwork, and knowledge application. Conclusions TBL appears feasible in the GME environment, with learner reactions ranging from positive to neutral. Gaps in the literature occur within each of the 4 elements of the suggested framework, including: system, faculty preparation time and minimum length of effective TBL sessions; residents, impact of team heterogeneity and inconsistent attendance; significance, comparison to other instructional methods and outcomes measuring knowledge retention, knowledge application, and skill development; and scaffolding, factors that influence the completion of preparatory work.
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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 | 0.055 | 0.153 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.029 | 0.020 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| 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".