The Influence of Income on Medical School Admissions in Canada: A Retrospective Cohort Study
Bibliographic record
Abstract
Abstract BACKGROUND. The socioeconomic status of applicants to Canadian medical schools has been understudied in the past two decades. Institutional efforts have been made to address the lack of socioeconomic diversity across Canada during this time. We investigated the income characteristics of medical school applicants, as well as the relationship between applicant income and offer of admission, to characterize the current state of socioeconomic diversity in medical admissions.METHODS. We conducted a retrospective cohort study on 26,120 applicants at one Ontario medical school from 2013 to 2018. Characteristics of applicants who were offered admission were compared to the general population and applicants not offered admission. Regression analyses were used to investigate the association between median total neighborhood income and successful admission, as well as performance on admission testing criteria, including: the MMI, CASPer, GPA, and the MCAT.RESULTS. The median total neighborhood income for medical school applicants was $98,816, which was approximately $28,480 higher than the Canadian general population. Those not admitted to the medical school had a median total neighborhood income of $98,304 compared to $105,984 for those offered admission (p<0.001). This trend was seen in every province and territory in Canada. Median total neighborhood income was an independent predictor of admission; applicants in the >75 th percentile income group had 54% increased odds of being offered admission when compared to applicants in the <25 th percentile. Performance on the MCAT, MMI and CASPer were moderately associated with income; applicants’ GPAs were not associated with income.CONCLUSION. Medical school applicants are from higher economic strata compared to the general population. Despite already representing a high economic stratum, a higher median total neighborhood income relative to other applicants was associated with an offer of admission.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.306 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".