Success of a Pre-medical Master's Degree Program in Preparing Students for Medical Careers
Bibliographic record
Abstract
A substantial number of qualified college graduates in the United States and Canada fail to secure admission to medical school because their undergraduate academic performance is not deemed to be competitive by medical school admissions committees. The aim of this report is to describe the success of the Master of Arts in Medical Sciences (MAMS) program at Boston University School of Medicine (BUSM) in preparing students with non-competitive undergraduate credentials to gain admission into medical school. Several independent sources of information about student performance including MAMS application data, Boston University academic transcripts, and medical school acceptance information were assembled in a unified database for this longitudinal study. The privacy of individual students was maintained and no names or other data that are individually identifiable are presented in this communication. As indicated herein, admission to both U.S. and non-U.S. medical schools (allopathic and osteopathic) are included in these analyses. We also examined the residency placements of a cohort of MAMS graduates who matriculated at the BUSM MD program (MAMS/MD), since these are the only students for whom we know the placements that resulted from the National Resident Match Program (NRMP). The MAMS program has been in existence for more than 25 years and has educated more than 2500 students with approximately 70% of graduates admitted to medical school. The MAMS/MD students performed well in medical school and secured high quality residency positions. Graduate programs in medical sciences in a medical school environment are important vehicles for preparing and increasing the pool of academically qualified medical school applicants who will become successful medical school students in the near term and effective practitioners in the long term.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".