Examining Differences in the Preparation and Performance of US MCAT Examinees from Lower-SES Backgrounds: Awareness, Access, and Action Insights to Narrow Learning Opportunity and Performance Gaps and Promote Learning for All Aspiring Physicians
Notice bibliographique
Résumé
Phenomenon: On the Medical College Admission Test (MCAT), required for entry into all medical schools in the U.S. and many in Canada, average scores are typically lower for individuals from lower socioeconomic status (SES) backgrounds compared to their more advantaged peers, although individuals from every background score in the lower, middle, and upper ranges of the score scale. This achievement gap is potentially due in part to disparities in resource utilization and effective study strategies. Viewing this challenge through a socioecological systems lens can help identify potential systems-level opportunities to support students from these backgrounds to succeed in medicine. Approach: This investigation was the first large-scale review of MCAT preparation strategies, resource utilization, and challenges for examinees from lower-SES backgrounds, focusing on those who obtained higher versus lower MCAT scores. It aimed to examine differences in students’ use of evidence-supported learning/studying strategies and challenges experienced in preparing for the MCAT exam. Survey data from the Association of American Medical Colleges Post-MCAT Questionnaire on MCAT preparation strategies and resources used and challenges experienced by 2021–2023 examinees were analyzed, focusing on the 3,240 survey respondents from lower-SES backgrounds. T-tests and chi-square analyses compared continuous variables and proportions between lower- and higher-scoring examinees from lower-SES backgrounds, using Cohen’s h to estimate effect size. Findings: Higher-scoring examinees reported greater use of many evidence-supported effective test preparation and learning strategies, including discussing preparation strategies with advisors/peers, establishing baseline capabilities, practicing applying knowledge to practice questions, and evaluating readiness by taking a practice test. Utilization rates of high-value, free/low-cost MCAT resources were significantly higher among top scorers. Conversely, lower-scoring examinees were more likely to report challenges in obtaining reliable internet access, determining how to begin studying, and accessing concrete information about the MCAT exam. Insights: This study highlights critical differences in preparation approaches and challenges among examinees from lower-SES backgrounds. Identifying these gaps may provide insights regarding interventions to improve access to resources and potential improvement to MCAT performance. We provide systems-level ideas for how to better support students from lower-SES backgrounds. For example, learning specialists and advisors could use the findings from this study to screen and educate examinees about evidence-based MCAT preparation strategies and resources. This study identifies opportunities to inform interventions to help students from lower-SES backgrounds advance toward a career in medicine.
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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 | 0,002 | 0,003 |
| 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 | 0,000 | 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 ».