Twelve tips for using Twitter as a learning tool in medical education
Bibliographic record
Abstract
BACKGROUND: Twitter is an online social networking service, accessible from any Internet-capable device. While other social networking sites are online confessionals or portfolios of personal current events, Twitter is designed and used as a vehicle to converse and share ideas. For this reason, we believe that Twitter may be the most likely candidate for integrating social networking with medical education. AIMS: Using current research in medical education, motivation and the use of social media in higher education, we aim to show the ways Twitter may be used as a learning tool in medical education. METHOD: A literature search of several databases, online sources and blogs was carried out examining the use of Twitter in higher education. RESULTS: We created 12 tips for using Twitter as a learning tool and organized them into: the mechanics of using Twitter, suggestions and evidence for incorporating Twitter into many medical education contexts, and promoting research into the use of Twitter in medical education. CONCLUSION: Twitter is a relatively new social medium, and its use in higher education is in its infancy. With further research and thoughtful application of media literacy, Twitter is likely to become a useful adjunct for more personalized teaching and learning in medical education.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".