974. The Use of Social Media for Medical Education During the COVID-19 Pandemic; A Vision to the Future
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,006 | 0,006 |
| Science ouverte | 0,000 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,018 | 0,003 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».