Exploring the usage of learning resources by medical students in the basic science stage and their effect on academic performance
Notice bibliographique
Résumé
BACKGROUND: The United States Medical Licensing Examination (USMLE) step 1 is one of the two examinations written after completion of the first two years (basic science stage) of medical school to be eligible to apply for residency training in the USA. A huge number and types of study materials are available to prepare for the exam which might confuse students choosing a resource. We investigated learning resources being used by the third and fifth-semester medical students and their association with academic performance. We also compared learning resources and exam scores of high-performing and low-performing students. METHODS: Data collection was done using structured (quantitative study) and semi-structured (qualitative study) questionnaires during a face-to-face interview. This article is about the quantitative part which was designed as a correlational study. Single factor one-way analysis of variance (ANOVA), Pearson correlation coefficient test, T-test, and Fisher's exact test were used to analyze the data. RESULTS: About half of all students used three or more commercial resources dealing with the same content. A weak negative correlation was observed between the number of commercial resources and the exam scores, especially when the number of these resources was three or more (r = -0.26). The mean exam score of textbook users was statistically significantly higher than the mean score of textbook non-users (p = 0.01). The usage of textbooks was statistically significantly higher in the cohort of top performers in comparison to the rest of the students (p = 0.006). In addition to less usage of textbooks, the mean number of review books was higher in the group of weakest students (2.84 versus 3.7; p = 0.75). CONCLUSIONS: Most students did not use professional textbooks and about half used too many commercial review resources. While the former fact was significantly associated with poor academic performance, the later fact had weak negative correlation with exam score. Pedagogical interventions are urgently needed to make the right type of learning resources available by making professional textbooks more USMLE-oriented and helping the students choose the best and right number of resources for optimum academic performance. By fulfilling the observed needs of the students in this way, they might feel empowered because of self-determination which will motivate studies.
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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 | 0,013 | 0,017 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,002 |
| 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 ».