The changing landscape in nephrology education in India
Bibliographic record
Abstract
Digital tools have revolutionized education in nephrology in India. All forms of in-person learning are moving online. Social media have taken over the world, with clinicians learning and promoting multidirectional education methods. E-learning is better equipped to keep up with the rapid pace of new knowledge generation and dissemination. The use of digital multimedia tools to enhance rapid learning is backed by science, viz., dual-coding theory. Digital tools such as Twitter, blogs, podcasts, YouTube, and Nephrology Simulator (NephSIM) have had an impact in facilitating nephrology education among medical professionals and the general public. Digital tools, such as NephMadness, have resulted in the gamification of nephrology learning. Social media usage by the nephrology community in India is growing at a rapid pace. Everyday Cases in Nephrology (#ECNeph), a monthly Twitter-based discussion focused on academically challenging clinical cases, has its origins in India. The Women in Nephrology, India (WIN-India) initiative is very active in facilitating digital education in India and has, in a short space of time, created phenomenal momentum. Furthermore, non-governmental organizations in India, such as the Kidney Warriors Foundation and the Multi Organ Harvesting Aid Network (MOHAN) Foundation, have successfully tapped into social media to educate and aid kidney disease patients. All technologies come with some drawbacks. Despite their acceptance and validation, digital tools have their own pitfalls. These relate to (1) accessibility and connectivity, (2) accuracy of the scientific information, (3) social media noise, and (4) patient privacy. All pitfalls of digital education can be addressed by avoiding excessive social media overload and adopting an appropriate peer-review process. It is advisable to seek written consent from patients whenever patient data are posted online, to avoid privacy issues.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".