Social Media in the Emergency Medicine Residency Curriculum: Social Media Responses to the Residents’ Perspective Article
Bibliographic record
Abstract
In July to August 2014, Annals of Emergency Medicine continued a collaboration with an academic Web site, Academic Life in Emergency Medicine (ALiEM), to host an online discussion session featuring the 2014 Annals Residents' Perspective article "Integration of Social Media in Emergency Medicine Residency Curriculum" by Scott et al. The objective was to describe a 14-day worldwide clinician dialogue about evidence, opinions, and early relevant innovations revolving around the featured article and made possible by the immediacy of social media technologies. Six online facilitators hosted the multimodal discussion on the ALiEM Web site, Twitter, and YouTube, which featured 3 preselected questions. Engagement was tracked through various Web analytic tools, and themes were identified by content curation. The dialogue resulted in 1,222 unique page views from 325 cities in 32 countries on the ALiEM Web site, 569,403 Twitter impressions, and 120 views of the video interview with the authors. Five major themes we identified in the discussion included curriculum design, pedagogy, and learning theory; digital curation skills of the 21st-century emergency medicine practitioner; engagement challenges; proposed solutions; and best practice examples. The immediacy of social media technologies provides clinicians the unique opportunity to engage a worldwide audience within a relatively short time frame.
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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 | 0.012 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.008 |
| 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".