Experience in the Use of Social Media in Medical and Health Education
Bibliographic record
Abstract
OBJECTIVES: Social media are online tools that allow collaboration and community building. Succinctly, they can be described as applications where "users add value". This paper aims to show how five educators have used social media tools in medical and health education to attempt to add value to the education they provide. METHODS: We conducted a review of the literature about the use of social media tools in medical and health education. Each of the authors reported on their use of social media in their educational projects and collaborated on a discussion of the advantages and disadvantages of this approach to delivering educational projects. RESULTS: We found little empirical evidence to support the use of social media tools in medical and health education. Social media are, however, a rapidly evolving range of tools, websites and online experiences and it is likely that the topic is too broad to draw definitive conclusions from any particular study. As practitioners in the use of social media, we have recognised how difficult it is to create evidence of effectiveness and have therefore presented only our anecdotal opinions based on our personal experiences of using social media in our educational projects. CONCLUSION: The authors feel confident in recommending that other educators use social media in their educational projects. Social media appear to have unique advantages over non-social educational tools. The learning experience appears to be enhanced by the ability of students to virtually build connections, make friends and find mentors. Creating a scientific analysis of why these connections enhance learning is difficult, but anecdotal and preliminary survey evidence appears to be positive and our experience reflects the hypothesis that learning is, at heart, a social activity.
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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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".