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Record W4409522867 · doi:10.1080/10401334.2025.2492620

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

2025· article· en· W4409522867 on OpenAlexaffabout
Aubrie Swan Sein, Stephanie C. McClure, Julie A. Chanatry, Daniel M. Clinchot, Liesel Copeland, Francie Cuffney, Rhona Beaton, Kadian McIntosh, Cynthia A. Searcy

Bibliographic record

VenueTeaching and Learning in Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsColumbia College
Fundersnot available
KeywordsPsychologyAction (physics)Medical educationMathematics educationPedagogyMedicinePhysics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.212
Threshold uncertainty score0.623

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.073
GPT teacher head0.367
Teacher spread0.294 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2025
Admission routes2
Has abstractyes

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