Effectiveness of Virtual Medical Teaching During the COVID-19 Crisis: Systematic Review
Notice bibliographique
Résumé
BACKGROUND: In December 2019, COVID-19 emerged and rapidly spread worldwide. Transmission of SARS-CoV-2, the virus that causes COVID-19, is high; as a result, countries worldwide have imposed rigorous public health measures, such as quarantine. This has involved the suspension of medical school classes globally. Medical school attachments are vital to aid the progression of students' confidence and competencies as future physicians. Since the outbreak of COVID-19, medical schools have sought ways to replace medical placements with virtual clinical teaching. OBJECTIVE: The objective of this study was to review the advantages and disadvantages of virtual medical teaching for medical students during the COVID-19 pandemic based on the current emerging literature. METHODS: A brief qualitative review based on the application and effectiveness of virtual teaching during the COVID-19 pandemic was conducted by referencing keywords, including medical student virtual teaching COVID-19, virtual undergraduate medical education, and virtual medical education COVID-19, in the electronic databases of PubMed and Google Scholar. A total of 201 articles were found, of which 34 were included in the study. Manual searches of the reference lists of the included articles yielded 5 additional articles. The findings were tabulated and assessed under the following headings: summary of virtual teaching offered, strengths of virtual teaching, and weaknesses of virtual teaching. RESULTS: The strengths of virtual teaching included the variety of web-based resources available. New interactive forms of virtual teaching are being developed to enable students to interact with patients from their homes. Open-access teaching with medical experts has enabled students to remain abreast of the latest medical advancements and to reclaim knowledge lost by the suspension of university classes and clinical attachments. Peer mentoring has been proven to be a valuable tool for medical students with aims of increasing knowledge and providing psychological support. Weaknesses of virtual teaching included technical challenges, confidentiality issues, reduced student engagement, and loss of assessments. The mental well-being of students was found to be negatively affected during the pandemic. Inequalities of virtual teaching services worldwide were also noted to cause differences in medical education. CONCLUSIONS: In the unprecedented times of the COVID-19 pandemic, medical schools have a duty to provide ongoing education to medical students. The continuation of teaching is crucial to enable the graduation of future physicians into society. The evidence suggests that virtual teaching is effective, and institutions are working to further develop these resources to improve student engagement and interactivity. Moving forward, medical faculties must adopt a more holistic approach to student education and consider the mental impact of COVID-19 on students as well as improve the security and technology of virtual platforms.
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 | 0,010 | 0,067 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,005 |
| Bibliométrie | 0,007 | 0,009 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 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 ».