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Enregistrement W3017137483 · doi:10.1096/fasebj.2020.34.s1.04297

Histology Retention in a Medical School Curriculum

2020· article· en· W3017137483 sur OpenAlexaff
Catherine Will, Anna Edmondson, Alexa Hryniuk

Notice bibliographique

RevueThe FASEB Journal · 2020
Typearticle
Langueen
DomaineEngineering
ThématiqueAnatomy and Medical Technology
Établissements canadiensUniversity of Manitoba
Organismes subventionnairesnon disponible
Mots-clésHistologyVirtual microscopyMedicineCurriculumMedical educationPsychologyPathologyPedagogy

Résumé

récupéré en direct d'OpenAlex

Introduction Poor retention of medical knowledge is a concern within medical education. While studies show that student retention from basic science disciplines often follows the “forgetting curve”, histology retention has not been examined independently of other anatomical sciences. Investigation of histology retention is of increasing importance as medical education moves towards integrated curricula with the use of technological advances in the classroom, such as virtual microscopy. The purpose of this study was to evaluate histology retention of first‐year students at the Medical College of Georgia (MCG). Aims The specific aims of this study include evaluating the relationships of 1) histology retention and academic performance 2) retention intervals (RI) to histology retention and 3) histology retention and students’ previous exposure to histology as well as their modality of study. Methods Academic performance data from histology quizzes and exams were collected from first‐year medical students at MCG from the Class of 2022 (n=171). A histology comprehensive assessment was administered at the end of the academic year to assess histology knowledge retained throughout first‐year histology curriculum. Students were also surveyed on their prior histology experience and study method modality. A linear regression analysis was performed to determine if there was a correlation between academic performance and retention. A comparison of means was used to assess the relationship between histology retention scores compared to academic performance in terms of RI, histology exposure, and modality of study. Paired sample t‐tests were used for analyses. IRB approval (exempt) was obtained from Augusta University. Results First‐year medical students at MCG were found to only retain 52.4% ± 17.0% of histology content on the end of year comprehensive assessment. Academic performance in histology did not predict retention at the end of the academic year (R=0.27). Student retention dropped on average from 84% to 52% regardless of RI length (2, 3, 5, or 6 months). No significant difference was found between students with prior histology exposure (85.9% ± 4.9%) and those without (84.2% ± 5.3%) on overall histology grade averages. However, those with prior histology experience did score significantly better on the comprehensive assessment (58.5% ± 15.3% vs. 51.2% ± 17.2%; p=0.04). No significant difference was seen on average histology grades (84.5% ± 4.2%, 84.2% ± 16.6%) or comprehensive assessment performance (52.9% ± 6.9%, 51.4% ± 17.8%) when study modalities (virtual microscopy vs physical slides) were compared. Discussion and Conclusions This data supports previously reported findings that medical students retain on average ~50% of their basic science knowledge. These findings also demonstrate that academic performance is not a predictor of retention. Furthermore, RI and method of study appear to have no significant impact on histology retention. However, prior histology experience appears to aid in retention and suggest that more testing/re‐exposure during the academic year could increase histology retention. These results may be useful in informing educators in medical education and help to guide reform for better retention outcomes from pre‐clinical medical curricula.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,615
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,011
Tête enseignante GPT0,224
Écart entre enseignants0,213 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations4
Publié2020
Routes d'admission1
Résumé présentoui

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