Rural medical students at urban medical schools: Too few and far between?
Bibliographic record
Abstract
BACKGROUND: Rural regions of industrialized nations are experiencing a crisis in health care access, reflecting a high disease burden and a low physician supply. The maldistribution of physicians stems partly from the low rate of entry into medical school of applicants from rural backgrounds. METHODS: We analyzed applicants to the University of Toronto medical school in 2005 (n = 2052) to test for possible institutional bias against rural applicants and possible applicant bias against the institution. The designation of rurality was assigned using the Statistics Canada classification of residential postal codes to detect residence in communities with a population of fewer than 10,000 people. RESULTS: Consistent with past reports, rural applicants were under-represented (n = 93, 4.5% of applicants relative to 20% of baseline population). Rural applicants, on average, were equally competitive with urban applicants as measured by grades, test scores, and interviews. Rural applicants were just as likely as urban applicants to be offered admission (17% vs 14%, p = 0.43), indicating no large bias from the institution. Rural applicants, however, were more than twice as likely to decline the admission offer (69% vs 24%, p < 0.001), indicating a large bias against the institution. This discrepancy was not explained by financial disparity and was not confined to those applicants most likely to receive invitations to other schools. CONCLUSIONS: Programs to increase physician supply in rural areas need to address students' concealed preferences that are established before enrolment. Medical schools, in particular, need to encourage more rural students to apply and to persuade those offered admission to accept.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".