Team-Based Learning From Theory to Practice: Faculty Reactions to the Innovation
Bibliographic record
Abstract
BACKGROUND: Limited studies have examined the factors associated with the implementation of team-based learning (TBL). PURPOSE: The purpose of this study was to identify faculty reactions (successes and challenges) associated with the implementation of a modified TBL in undergraduate anatomy teaching. METHOD: To obtain faculty reactions to the TBL approach, data collection included focus groups, observations, and document analysis. Using the constant comparative method, our analysis yielded four key themes. RESULTS: Four themes based on faculty reactions to the implementation of TBL included transportability and local adaptations, faculty/tutor role confusion, student preparedness, and teacher-targeted bullying. CONCLUSIONS: Future physicians will need educational programs that embrace the theory and practice of teamwork. Schools adopting team-based learning approaches will need to carefully consider their local environments so as to successfully transport innovative practices alongside local adaptations. As front-line implementers faculty will require initial and ongoing professional development. The TBL method is amenable to local modifications and holds promise as a pedagogical strategy to garner increased student engagement and student achievement in their learning.
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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 | 0.020 | 0.100 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".