974. The Use of Social Media for Medical Education During the COVID-19 Pandemic; A Vision to the Future
Bibliographic record
Abstract
Abstract Background The COVID-19 is the first pandemic in history where technology and social media can be used to keep people safe and informed. The correct management of information has been recognized as a critical part of controlling the COVID-19 pandemic. The objective of this study is to create a source of information about COVID-19 that is reliable, accessible, and easy to share while providing literature references. Methods An Instagram account named @cienciacontracovid19 was created in 2020. In this account, the most relevant up-to-date medical information of COVID-19 is published daily in Spanish. All the account’s content is made by two infectious diseases specialists and a general practitioner. After 6 months since the creation of the account, we performed a survey to assess the followers perception of the usefulness of @cienciacontracovid19 during the pandemic. Results The account was opened in November 2020. Figure 1 QR to access. Currently, the account has 9,534 followers from 5 Latin-American countries; 48% are between 25-34 years old, 76.6% are women, and 52% are healthcare workers. Until May 2021, 142 educational slides, 3 educational videos and 5 webinars have been posted. In the last 30 days, @cienciacontracovid19 has had 10,540 interactions and growth of +125% reaching 22,000 users. We conducted a survey in April 2021, in which 3,556 people answered. The following results were obtained: 76% considered that the information was always useful in their daily lives and 17% frequently useful. 77% affirmed that the information shared was always reliable and 47% consider that the information differed from other sources of information since it is easy to understand and 34% because it has bibliographic references to support it. 85% responded that the information shared in the account kept them from putting themselves at risk. When asking if the information shared has made them feel safer by being informed, 49% answered always and 44% frequently. QR to access the instagram account Conclusion @cienciacontracovid19 has been a valuable source of scientific information with a positive impact on its users. Its implementation has been a practical medical education tool during the COVID-19 pandemic. By being informed, people could potentially modify some of their behaviors to stay out of risk from COVID-19. Disclosures All Authors: No reported disclosures
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".