Are We Effectively Teaching the Basic Sciences? The Influence of Pedagogical Methods on 3rd Year Medical Students' Basic Science Knowledge Retention in an Undergraduate Medical Education Curriculum
Notice bibliographique
Résumé
Introduction Basic sciences (anatomy, biochem., microbio., path., pharm., phys.) are essential in undergraduate medical education (UME), as they provide a framework on which clinical concepts can be built. However, studies demonstrate that students' basic science knowledge retention can drop to as low as 46% within 10 months. Additionally, the pedagogical methods used for teaching the basic sciences varies widely among institutions, and research indicates that knowledge retention varies with pedagogical methods. Active learning is typically related to higher retention rates compared to didactic lectures; however, some studies have shown that this is not always the case. Currently, it is unclear if students learn and retain the most relevant basic science concepts from pre‐clerkship, and if current UME curricula are employing the most effective pedagogical methods to teach the basic sciences. Thus, this study aims to: 1) assess clerks' basic science knowledge level prior to and at the completion of each clerkship rotation, and 2) evaluate the effectiveness of the current pedagogical methods employed in Schulich's basic science pre‐clerkship curriculum. Methods 1) A basic science assessment comprised of clinical vignette MCQ's was created for each clerkship rotation (family med., psych., paeds., OBGYN, surgery, internal med.). Assessments were distributed as a pre‐ and post‐test for each rotation to assess clerks' knowledge retention and learning. 2) The assessed basic science concepts were then mapped and correlated to the pedagogical method in which the concept was taught. Results 1) Assessment data revealed that clerks achieved an average passing grade (≥60%) on each pre‐test. There was a significant increase between pre‐ and post‐test scores for all rotations (p≤0.05); however, the greatest improvement (10%) was identified for clerks completing paeds (p=0.001). 2) Mapping data elucidated that the majority of the assessed basic science concepts are currently being taught via didactic lectures (60–83%), while 11–27% are taught using active learning methods. There was a strong correlation between the percent of didactic lectures and student assessment scores (R 2 =.890, p=.005), while active learning methods only had a moderate correlation (R 2 =.668, p=.047). Conclusions Results indicate that clerks have an adequate knowledge of basic science concepts prior to each rotation, and further learned concepts during clerkship. The large increase identified in paeds. may reflect the high volume of in‐class instruction that occurs during that rotation. Additionally, although Schulich UME currently employs both active and didactic methods, data indicate that students achieved the highest retention scores on basic science concepts taught primarily via didactic lectures. This may be due to the fact that students are familiar with learning the basic sciences didactically, and the associated MCQ testing style. Thus, our data suggest that didactic lectures are an effective way to promote knowledge acquisition and retention in Schulich's basic science pre‐clerkship curriculum. Support or Funding Information AAA, OGS This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,011 | 0,068 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 source (Gemma direct ou Codex distillé), 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 ».