<i>JGME</i> -ALiEM Hot Topics in Medical Education: Analysis of a Multimodal Online Discussion About Team-Based Learning
Bibliographic record
Abstract
BACKGROUND: Team-based learning (TBL) is an instructional method that is being increasingly incorporated in health professions education, although use in graduate medical education (GME) has been more limited. OBJECTIVE: To curate and describe themes that emerged from a virtual journal club discussion about TBL in GME, held across multiple digital platforms, while also evaluating the use of social media in online academic discussions. METHODS: article "Use of Team-Based Learning Pedagogy for Internal Medicine Ambulatory Resident Teaching." Using 4 stimulus questions (hosted on a blog as a starting framework), we facilitated discussions via the blog, Twitter, and Google Hangouts on Air platforms. We evaluated 2-week web analytics and performed a thematic analysis of the discussion. RESULTS: The virtual journal club reached a large international audience as exemplified by the blog page garnering 685 page views from 241 cities in 42 countries. Our thematic analysis identified 4 domains relevant to TBL in GME: (1) the benefits and barriers to TBL; (2) the design of teams; (3) the role of assessment and peer evaluation; and (4) crowdsourced TBL resources. CONCLUSIONS: The virtual journal club provided a novel forum across multiple social media platforms, engaging authors, content experts, and the health professions education community in a discussion about the importance, impediments to implementation, available resources, and logistics of adopting TBL in GME.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".