Social media in medical education: a new pedagogical paradigm?
Bibliographic record
Abstract
Social media is now a part of modern life.1 Internet based tools allow millions to keep in touch with each other and anyone to create, and publish content instantly. Individuals enjoy the fun, and rely on the functionality of social media in their daily lives. But the real power of social media is the impact of bringing together clusters of like-minded people to engage in real-time, on-line dialogue on topics that merely interest them—or about which they feel passionately: ‘community’ is no longer a function of geography. Crowd-sourced funding initiatives for start-up companies, and attempts to influence government or corporate policy through petitions ‘signed’ by thousands in a matter of days, are normal aspects of enterprise today. The Arab Spring is perhaps the most notable example of the potential impact of social media and shows how connecting thousands of people, in real time, can raise activism from a local concern to a worldwide movement.2 Such developments are way beyond the expectations of the small group of academics who, in 1989, invented the internet to improve communication between scientists.3 Their altruism, and insistence that the World Wide Web should be available ‘free’ to anyone on the planet, laid the foundation of today's developments. Social media has yet to have the same impact on medical practice as they are having on daily life. However, the internet is making a difference. Knowledge, once the monopoly of the professions is now available 24/7 to anyone with a search engine. Healthcare professionals must get used to losing this monopoly or they won't be able to function in today's world. Inexorably, the tenor of consultations is changing: doctors—once suppliers …
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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 | 0.012 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.006 |
| 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; both teacher heads agree on what is shown here.
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".