Shifts in Digital Resources Usage for Gross Anatomy Education During Covid‐19
Notice bibliographique
Résumé
INTRODUCTION/OBJECTIVE: Covid‐19 has led to sudden changes to gross anatomy education when traditional dissection‐based laboratories had to shift towards virtual modalities due to physical distancing and remote learning requirements. The purpose of this study was to determine how the use of digital teaching resources in gross anatomy education changed from before to during Covid‐19. MATERIAL/METHODS: Data were obtained from an IRB‐approved survey distributed to professional associations and listservs targeting anatomy educators from June to November 2020. Respondents were asked to select the digital resources they used before and during Covid‐19. Data were analyzed during the early and latter parts of the pandemic as May‐August (T1) and August‐December (T2), as well as overall (T3). T2 data were classified into five categories: 2D illustrations, dissection media, interactive software, in‐house, and open access. Total usage for each timepoint, the proportions of digital resources, and the 5 categories before and during Covid‐19 were compared using McNemar's test with alpha<5%. Data are presented as percent increase (+value) or decrease (‐value). RESULTS: 60 and 208 responses were received for T1 and T2, respectively. The total number of digital resources used for anatomy education increased from before to during COVID‐19 as seen in the data analysis from T1 (+47%), T2 (+41%), and T3 (+43%) (P≤0.003). In T1, the use of BlueLink (+122%) and Complete Anatomy (+140%) software increased (P<0.04), while Acland's Anatomy (+68%), Anatomy.TV (+1300%), Complete Anatomy (+89%), and BlueLink (+148%) increased in T2 (P≤0.03). During T3, the usage of Acland's Anatomy (+60%), Anatomy.TV (+750%), Complete Anatomy (+102%), BlueLink (+143%), and VisibleBody (+100%) increased (P≤0.03). All other digital resources did not change (P>0.05). When data for T2 were categorized, dissection media (+44%), interactive software (+87%), and open‐access (+100%) content increased (P≤0.008), while 2D illustrations (‐3%) and in‐house content (‐23%) decreased (P>0.05). CONCLUSIONS: This study demonstrates sustained increases in digital resource usage for gross anatomy education during Covid‐19. This was particularly pronounced for interactive software, open access resources and dissection media that allowed educators to mimic features of a dissection lab. SIGNIFICANCE: These rapid shifts in both commercial and free digital resources are likely to drive innovation in anatomy education for years to come. It remains unknown if the current findings are transient Covid‐19‐related changes or if they will persist long‐term.
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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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 ».