Who gets into medical school? Comparison of students from rural and urban backgrounds.
Bibliographic record
Abstract
OBJECTIVE: Being of rural origin is one of the few predictors of whether medical students choose either family or rural practice as a career. This study investigates what proportion of applicants are of rural origin, what their grades are, and whether they are accepted. DESIGN: Mailed survey using the postal codes of Ontario medical school applicants' residences when they attended secondary school to link them to communities. Applicants of rural origin were defined as having attended secondary school while residing in communities with core populations of fewer than 10 000 people. SETTING: Province of Ontario, its six medical schools, and its 1 500 000 rural citizens (13% of the total population). PARTICIPANTS: All 4948 applicants to Ontario medical schools in 2002 and 2003 who had gone to high school in Ontario. MAIN OUTCOME MEASURES: Proportion of rural applicants among all applicants in the given years. Mean grade point averages (GPA) and Medical College Admission Test (MCAT) scores attained by applicants of both urban and rural origin. Proportion of rural students among all students admitted to medical schools. RESULTS: While 13% of the Ontario population is rural, only 7.3% of Ontario applicants to medical school were of rural origin (P < .001). On average, the GPAs of applicants of rural and urban origin were identical at 3.42 (P = .995 not significant [NS]). The MCAT scores averaged 8.9 for applicants of rural origin and 9.0 for applicants of urban origin (P = .36 NS). Applicants of rural origin were admitted to medical school as frequently as applicants of urban origin (1:5.6 vs 1:4.7, P = .139 NS). CONCLUSION: Although students of rural origin in Ontario apply to medical school less frequently than students of urban origin do, those that do apply have similar grades to those of urban applicants and are equally likely to be accepted.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".