The Social Media Revolution in Nephrology Education
Bibliographic record
Abstract
The past decade has been marked by the increasing use of social media platforms, often on mobile devices. In the nephrology community, this has resulted in the organic and continued growth of individuals interested in using these platforms for education and professional development. Here, we review several social media educational resources used in nephrology education and tools including Twitter, videos, blogs, and visual abstracts. We will also review how these tools are used together in the form of games (NephMadness), online journal clubs (NephJC), interactive learning (GlomCon), and digital mentorship (Nephrology Social Media Collective [NSMC] Internship) to build unique educational experiences that are available globally 24 hours per day. Throughout this discussion, we focus on specific examples of free open-access medical education (FOAMed) tools that provide education and professional growth at minimal or no cost to the user. In addition, we discuss inclusion of FOAMed resource development in the promotion and tenure process, along with potential pitfalls and future directions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.003 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".