Navigating Your First Cut: A Hands‐off Animated Guide to the First Year Anatomy Lab
Notice bibliographique
Résumé
Knowledge of gross anatomy is a critical component of a medical student's education. An understanding of human anatomy is equally essential for the practicing clinician. Historically, schools have solely used cadaver dissections to teach the anatomical sciences. However, lab sessions can be overwhelming, in particular the first session. Students in their first year of medical school are confronted with so many unknowns, and they struggle to prepare mentally and academically for the experience. Where do you cut? What is underneath, beside, above, or below what you are cutting? What are you looking for? A learner, who is yet unfamiliar with the terms, may find written instructions for the dissection difficult to understand. So how does one enhance learning of gross anatomy? In recent years, many medical schools have supplemented cadaver dissections with multimedia, where as others have completely replaced cadaver dissections with prosections or multimedia. We pursued the former method to improve learning: creating an animated video guide for the first gross anatomy dissection in the first year medical undergraduate curriculum. The video takes the learners through the technical and academic steps of their first dissection. And for many students, the video was their first real look at what dissection looks and sounds like. The aim was to alleviate some of the anxieties as students enter this transformative component of their education. We surveyed the students before and after they completed the lab to understand whether the video was helpful. An overwhelming majority of respondents rated the video as very helpful in preparing them for this experience. Some of the comments included: “It was really helpful to see a cadaver being cut before having to do [the dissection] to help mentally prepare” and “The video could not have prepared me better”. In addition, nearly all respondents said they would like to see more videos in the future. Given its reception, we conclude that this visual guide served to optimize the students' gross anatomy experience, and thus their learning and understanding of the material.
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 | 0,001 | 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 | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 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 ».