Evidence-based medicine in the era of social media: Scholarly engagement through participation and online interaction
Bibliographic record
Abstract
The integration of new knowledge into clinical practice continues to lag behind discovery. The use of Free Open Access Medical education (FOAM) has disrupted communication between emergency physicians, making it easy for practicing clinicians to interact with colleagues from around the world to discuss the latest and highest impact research. FOAM has the potential to decrease the knowledge translation gap, but the concerns raised about its growing influence are 1) research that is translated too quickly may cause harm if its findings are incorrect; 2) there is little editorial oversight of online material; and 3) eminent online individuals may develop an outsized influence on clinical practice. We propose that new types of scholars are emerging to moderate the changing landscape of knowledge translation: 1) critical clinicians who critically appraise research in the same way that lay reviewers critique restaurants; 2) translational teachers adept with these new technologies who will work with researchers to disseminate their findings effectively; and 3) interactive investigators who engage with clinicians to ensure that their findings resonate and are applied at the bedside. The development of these scholars could build on the promise of evidence-based medicine by enhancing the appraisal and translation of research in practice.
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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 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.017 | 0.020 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".