The Validity of Scores From the New MCAT Exam in Predicting Student Performance: Results From a Multisite Study
Bibliographic record
Abstract
PURPOSE: The new Medical College Admission Test (MCAT) was introduced in April 2015. This report presents findings from the first study of the validity of scores from the new MCAT exam in predicting student performance in the first year of medical school (M1). METHOD: The authors analyzed data from the national population of 2016 matriculants with scores from the new MCAT exam (N = 7,970) and the sample of 2016 matriculants (N = 955) from 16 medical schools who volunteered to participate in the validity research. They examined correlations of students' MCAT total scores and total undergraduate grade point averages (UGPAs), alone and together, with their summative performance in M1, and the success rate of students with different MCAT scores in their on-time progression to the second year of medical school (M2). They assessed whether MCAT scores provided comparable prediction of performance in M1 by students' race/ethnicity, socioeconomic background, and gender. RESULTS: Correlations of MCAT scores with summative performance in M1 ranged from medium to large. Although MCAT scores and UGPAs provided similar prediction of performance in M1, using both metrics provided better prediction than either alone. Additionally, students with a wide range of MCAT scores progressed to M2 on time. Finally, MCAT scores provided comparable prediction of performance in M1 for students from different sociodemographic backgrounds. CONCLUSIONS: This study provides early evidence that scores from the new MCAT exam predict student performance in M1. Future research will examine the validity of MCAT scores in predicting performance in later years.
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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".