Social Media and Medical Education in the Context of the COVID-19 Pandemic: Scoping Review
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has brought virtual web-based learning to the forefront of medical education as training programs adapt to physical distancing challenges while maintaining the rigorous standards of medical training. Social media has unique and partially untapped potential to supplement formal medical education. OBJECTIVE: The aim of this review is to provide a summary of the incentives, applications, challenges, and pitfalls of social media-based medical education for both trainees and educators. METHODS: We performed a literature review via PubMed of medical research involving social media platforms, including Facebook, Twitter, Instagram, YouTube, WhatsApp, and podcasts. Papers were reviewed for inclusion based on the integrity and power of the study. RESULTS: The unique characteristics of social media platforms such as Facebook, Twitter, Instagram, YouTube, WhatsApp, and podcasts endow them with unique communication capabilities that serve different educational purposes in both formal and informal education settings. However, contemporary medical education curricula lack widespread guidance on meaningful use, application, and deployment of social media in medical education. CONCLUSIONS: Clinicians and institutions must evolve to embrace the use of social media platforms for medical education. Health care professionals can approach social media engagement in the same ethical manner that they would with patients in person; however, health care institutions ultimately must enable their health care professionals to achieve this by enacting realistic social media policies. Institutions should appoint clinicians with strong social media experience to leadership roles to spearhead these generational and cultural changes. Further studies are needed to better understand how health care professionals can most effectively use social media platforms as educational tools. Ultimately, social media is here to stay, influencing lay public knowledge and trainee knowledge. Clinicians and institutions must embrace this complementary modality of trainee education and champion social media as a novel distribution platform that can also help propagate truth in a time of misinformation, such as the COVID-19 pandemic.
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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 | 0.006 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 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".