An Inquiry-Based Distance Learning Tool for Medical Students Under Lockdown (“COVID-19 Rounds”): Cross-Sectional Study
Notice bibliographique
Résumé
BACKGROUND: The COVID-19 pandemic presented significant challenges to both clinical practice and the delivery of medical education. Educators and learners implemented novel techniques, including distance learning and web-based rounds, while trying to stay updated with the surge of information regarding COVID-19 epidemiology, pathogenesis, and treatment. Hence, we designed and implemented a technologically enhanced course called "COVID-19 Rounds" to educate students about the rapidly evolving pandemic. OBJECTIVE: The objectives of this study are to describe a technologically enhanced course called "COVID-19 Rounds" and evaluate the following: (1) student satisfaction and program usefulness in achieving preset objectives, (2) perceived improvement in literacy regarding the pandemic, and (3) the impact of student engagement by designing infographics and initiating COVID-19-related research projects. METHODS: This is a cross-sectional study measuring the impact of the implementation of the web-based "COVID-19 Rounds" course. This program included web-based clinical experiences with physicians on actual rounds in COVID-19 wards in the hospital, weekly updates on evolving data and new research, and engagement in student-led projects. The study population included 47 fourth-year medical students at the Mohamed Bin Rashid University of Medicine and Health Sciences in Dubai, the United Arab Emirates, who attended the course. We designed and administered a 47-item survey to assess student satisfaction, program usefulness, impact on knowledge, and student engagement. Data were collected at the end of program delivery via Microsoft Forms. RESULTS: In total, 38 (81%) out of 47 fourth-year medical students participated in this study. The final course evaluation revealed an overall high satisfaction rate, with a mean rating of 3.9 (SD 0.94) on the 5-point Likert scale. Most students were satisfied with the course format (27/38, 71%), organization (31/38, 82%), and the learning experience (28/38, 74%) that the course offered. The course was particularly appreciated for offering evidence-based talks about aspects of the pandemic (34/38, 90%), providing weekly updates regarding emerging evidence (32/38, 84%), and enhancing understanding of the challenges of the pandemic (34/38, 90%). Satisfaction with distance learning was moderate (23/37, 62%), and a minority of students would have preferred an in-person version of the course (10/37, 27%). Student engagement in the course was high. All students participated in small group presentations of infographics of pandemic-related topics. Perceived advantages included conciseness and visual appeal, and disadvantages included the lack of detail and the time-consuming nature of infographic design, especially for students with no prior design experience. After the course ended, 27 (57%) students began research projects. This resulted in 6 abstracts presented at local meetings and 8 scientific papers published or submitted for publication. CONCLUSIONS: This inquiry-based adaptive approach to educating medical students about updates on COVID-19 via web-based learning was successful in achieving objectives and encouraging engagement in research. However, shortcomings of the course related to the lack of in-person teaching and clinical activities were also highlighted.
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 | 0,005 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
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 ».