A geographic-location-based medical school admissions process does not influence pre-clerkship and licensing examination academic performance
Bibliographic record
Abstract
Background: Students are selected for admission to the Northern Ontario School of Medicine University (NOSM U) MD degree program using criteria aiming to maximize access of persons thought most likely to practice in the region, including use of a geographic context score (GCS) which ranks those with lived experience in northern Ontario and/or rurality most highly. This study investigates the effect of this admissions process upon medical school academic performance. Methods: We used a retrospective cohort design combined with multiple linear regression analysis to investigate the relationship between admission scores and performance on pre-clerkship courses, and the Medical Council of Canada Qualifying Exam Part 1 (MCCQE1).The GCS did not significantly explain performance variance on any pre-clerkship course, nor on the MCCQE1, while the undergraduate Grade Point Average correlated with most assessment scores. The number of prior undergraduate biomedical courses predicted science and clinical skills performance, particularly in Year 1, but not with MCCQE1 scores. Performance on Year 2 courses, particularly foundational sciences and clinical skills, significantly predicted MCCQE1 scores. Results: Our data suggest that admission geographic context scoring is unrelated to future academic performance. Further, students with fewer prior undergraduate biomedical courses may benefit from increased support and/or a modified program during the early 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 | 0.002 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".