Using Instagram to Enhance a Hematology and Oncology Teaching Module During the COVID-19 Pandemic: Cross-sectional Study
Notice bibliographique
Résumé
BACKGROUND: The COVID-19 pandemic necessitated the rapid expansion of novel tools for digital medical education. At our university medical center, an Instagram account was developed as a tool for medical education and used for the first time as a supplement to the hematology and medical oncology teaching module of 2020/2021. OBJECTIVE: We aimed to evaluate the acceptance and role of Instagram as a novel teaching format in the education of medical students in hematology and medical oncology in the German medical curriculum. METHODS: To investigate the role of Instagram in student education of hematology and medical oncology, an Instagram account was developed as a tie-in for the teaching module of 2020/21. The account was launched at the beginning of the teaching module, and 43 posts were added over the 47 days of the teaching module (at least 1 post per day). Five categories for the post content were established: (1) engagement, (2) self-awareness, (3) everyday clinical life combined with teaching aids, (4) teaching aids, and (5) scientific resources. Student interaction with the posts was measured based on overall subscription, "likes," comments, and polls. Approval to conduct this retrospective study was obtained from the local ethics commission of the University Medical Center Goettingen. RESULTS: Of 164 medical students, 119 (72.6%) subscribed to the Instagram account, showing high acceptance and interest in the use of Instagram for medical education. The 43 posts generated 325 interactions. The highest number of interactions was observed for the category of engagement (mean 15.17 interactions, SD 5.01), followed by self-awareness (mean 14 interactions, SD 7.79). With an average of 7.3 likes per post, overall interaction was relatively low. However, although the category of scientific resources garnered the fewest likes (mean 1.86, SD 1.81), 66% (27/41) of the student participants who answered the related Instagram poll question were interested in studies and reviews, suggesting that although likes aid the estimation of a general trend of interest, there are facets to interest that cannot be represented by likes. Interaction significantly differed between posting categories (P<.001, Welch analysis of variance). Comparing the first category (engagement) with categories 3 to 5 showed a significant difference (Student t test with the Welch correction; category 1 vs 3, P=.01; category 1 vs 4, P=.01; category 1 vs 5, P=.001). CONCLUSIONS: Instagram showed high acceptance among medical students participating in the hematology and oncology teaching curriculum. Students were most interested in posts on routine clinical life, self-care topics, and memory aids. More studies need to be conducted to comprehend the use of Instagram in medical education and to define the role Instagram will play in the future. Furthermore, evaluation guidelines and tools need to be developed.
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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 | 0,003 | 0,035 |
| 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 | 0,002 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».