Have rural background students been disadvantaged by the medical school admission process?
Bibliographic record
Abstract
CONTEXT: The proportion of rural background applicants to medical school does not reflect the proportion of the general population that resides in rural communities. It is also believed that rural background applicants are disadvantaged by the admission processes of urban-based medical schools. This study sought to examine how rural background applicants actually fare as they progress through the stages of admission. METHODS: A 10-year cohort of Alberta applicants were tracked as they progressed through the interview and admittance stages. Background, grade point averages (GPAs) and Medical College Admission Test (MCAT) scores plus interview ratings were utilised in the analysis. RESULTS: Of the 4407 applicants, 1138 were interviewed. Significantly greater proportions of urban (26.8%) and rural (29.1%) background applicants than regional background (21.0%) applicants were interviewed, although the GPAs and MCAT scores of regional background applicants did not differ from those of the other applicant groups. Of those interviewed, 463 applicants were admitted. The proportions of urban (39.9%), regional (42.3%) and rural (46.3%) background applicants admitted were similar. Reviewers did not rate the files of urban, regional and rural background applicants differently. Only 6.7% of applicants admitted had rural backgrounds. DISCUSSION: Rural background applicants were not disadvantaged or discriminated against by the admission process. Although the backgrounds of applicants admitted reflect those of the original applicants, the proportion of rural background applicants is below the proportion of Albertans who reside in rural areas. Strategies to increase the number of applicants of rural origin are recommended.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 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; both teacher heads agree on what is shown here.
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".