Investigating Group Differences in Examinees’ Preparation for and Performance on the New MCAT Exam
Bibliographic record
Abstract
In 2015, the Medical College Admission Test (MCAT) was redesigned to better assess the concepts and reasoning skills students need to be ready for the medical school curriculum. During the new exam's design and rollout, careful attention was paid to the opportunities examinees had to learn the new content and their access to free and low-cost preparation resources. The design committee aimed to mitigate possible unintended effects of the redesign, specifically increasing historical mean group differences in MCAT scores for examinees from lower socioeconomic status (SES) backgrounds and races/ethnicities underrepresented in medicine compared with those from higher SES backgrounds and races/ethnicities not underrepresented in medicine.In this article, the authors describe the characteristics and scores of examinees who took the new MCAT exam in 2017 and compare those trends with historical ones from 2013, presenting evidence that the diversity and performance of examinees has remained stable even with the exam's redesign. They also describe the use of free and low-cost MCAT preparation resources and MCAT preparation courses for examinees from higher and lower SES backgrounds and who are enrolled in undergraduate institutions with more and fewer resources, showing that examinees from lower SES backgrounds and who attend institutions with fewer resources use many free and low-cost test preparation resources at lower rates than their peers. The authors conclude with a description of the next phase of this research: to gather qualitative and quantitative data about the preparation strategies, barriers, and needs of all examinees, but especially those from lower SES and underrepresented racial/ethnic backgrounds.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".