<i>JGME</i> -ALiEM Hot Topics in Medical Education Online Journal Club: An Analysis of a Virtual Discussion About Resident Teachers
Bibliographic record
Abstract
BACKGROUND: In health professionals' education, senior learners play a key role in the teaching of junior colleagues. OBJECTIVE: We describe an online discussion about residents as teachers to highlight the topic and the online journal club medium. METHODS: In January 2015, the Journal of Graduate Medical Education (JGME) and the Academic Life in Emergency Medicine blog facilitated an open-access, online, weeklong journal club on the JGME article "What Makes a Great Resident Teacher? A Multicenter Survey of Medical Students Attending an Internal Medicine Conference." Social media platforms used to promote asynchronous discussions included a blog, a video discussion via Google Hangouts on Air, and Twitter. We performed a thematic analysis of the discussion. Web analytics were captured as a measure of impact. RESULTS: The blog post garnered 1324 page views from 372 cities in 42 countries. Twitter was used to endorse discussion points, while blog comments provided opinions or responded to an issue. The discussion focused on why resident feedback was devalued by medical students. Proposed explanations included feedback not being labeled as such, the process of giving delivery, the source of feedback, discrepancies with self-assessment, and threats to medical student self-image. The blog post resulted in a crowd-sourced repository of resident teacher resources. CONCLUSIONS: An online journal club provides a novel discussion forum across multiple social media platforms to engage authors, content experts, and the education community. Crowd-sourced analysis of the resident teacher role suggests that resident feedback to medical students is important, and barriers to student acceptance of feedback can be overcome.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".