Histology Retention in a Medical School Curriculum
Notice bibliographique
Résumé
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 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 | 0,001 |
| 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 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».