<i>JGME</i> -ALiEM Hot Topics in Medical Education: Analysis of a Multimodal Online Discussion About Team-Based Learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it