The effectiveness of team-based learning on learning outcomes in health professions education: BEME Guide No. 30
Bibliographic record
Abstract
BACKGROUND: Team-Based Learning (TBL) is a student-centred active learning method, requiring less faculty time than other active learning methods. While TBL may have pedagogical value, individual studies present inconsistent findings. The aim of this systematic review was to assess the effectiveness of TBL on improving learning outcomes in health professions education. METHODS: A peer-reviewed systematic review protocol was registered with the Best Evidence in Medical Education (BEME) organization. After comprehensive literature searching, title and full-text review were completed by two independent reviewers. Included studies assessed TBL and a valid comparator in health professions. Included studies were assessed for methodological quality by two independent reviewers. Studies were categorised by outcomes using the Kirkpatrick framework. RESULTS: Of 330 screened titles, 14 were included. Seven studies reported significant increase in knowledge scores for the TBL group, four reported no difference and three showed improvement but did not comment on statistical significance. Only one study reported significant improvement in learner reaction for the TBL group while another study reported a significant difference favouring the comparator. CONCLUSIONS: Despite improvement in knowledge scores, there was mixed learner reaction. This may reflect the increased demands on learners in this student-centred teaching strategy, although further study is needed.
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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 | 0.015 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".