Level of Knowledge in the COVID-19 Pandemic: A Cross-Sectional Survey of Canadian Medical Students
Notice bibliographique
Résumé
Abstract Background During health crises medical education is often derailed as was the case during the current COVID-19 pandemic. Medical trainees face the daunting task of having to gather, filter and synthesize new information about the evolving situation often without the standardized resources they are used to. Methods We surveyed Canadian medical students, in the hardest hit province of Quebec, on how they were acquiring knowledge as well as what they knew of the pandemic. Google Forms was used, with the survey being distributed to each medical school in Quebec (McGill, ULaval, Udem) both through email and through social media pages for each class year. Two analyses, Mann-Whitney and ANOVA tests, were performed for year of study and degree obtained. Results We received responses from 111 medical students from three universities, which represents 5% of the students invited to complete the survey. Students reported using mass media most frequently (83%) and also had a high rate of use of social media (to gather information about the pandemic. They rated these resources low in terms of their trustworthiness despite the high rates of use (average 2.91 and 2.03 of 5 respectively). Medical students also endorsed using more formal resources like public health information, scientific journals and faculty-provided information that they trusted more, however, they accessed these resources at lower rates. Of note, medical students had correct answered 60% of COVID-19 prevention strategies, 73% clinical correct answers, 90% epidemiological correct answers. Additionally, students who were training in the larger city of Montreal, where the worst of the outbreak was focused, tended to significantly perform better (p Conclusion These finding indicate a wide use of information resources intended for public consumption rather than more rigorous and trustworthy sources. Furthermore, there seems to be a knowledge gap amongst medical students responding to this survey that suggests an opportunity to improve the delivery of educational content during this rapidly evolving pandemic.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,000 |
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 tête enseignante, 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 ».