Social media and medical education: Exploring the potential of Twitter as a learning tool
Bibliographic record
Abstract
This study set out to explore the ways in which social media can facilitate learning in medical education. In particular we were interested in determining whether the use of Twitter during an academic conference can promote learning for participants. The Twitter transcript from the annual International Conference on Residency Education (ICRE) 2013 was qualitatively analysed for evidence of the three overarching cognitive themes: (1) preconceptions, (2) frameworks, and (3) metacognition/refl ection in regard to the National Research Council ’ s (NRC) How People Learn framework . Content analysis of the Twitter transcript revealed evidence of the three cognitive themes as related to how people learn. Twitter appears to be most effective at stimulating individuals ’ preconceptions, thereby engaging them with the new material acquired during a medical education conference. The study of social media data, such as the Twitter data used in this study, is in its infancy. Having established that Twitter does hold signifi cant potential as a learning tool during an academic conference, we are now in a better position to more closely examine the spread, depth, and sustainability of such learning during medical education meetings.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".