Increasing the enrolment of rural applicants to the faculty of medicine and addressing diversity by using a priority matrix approach to assign values to rural attributes
Bibliographic record
Abstract
In an external review of the admissions process for the Faculty of Medicine, University of Manitoba, Canada, it was suggested that admissions policies be modified to increase the enrolment of students more likely to practise in rural locations, by selecting a cohort of students with attributes reflecting potential for rural practice. A broad-based Working Group devised a framework for scoring personal attributes reflecting a potential for living and working in rural areas. This framework, based on established characteristics reported in the literature, valued applicants who had rural connections, a history of rural employment, a history of rural community service, or a combination of these attributes. Relative weights for the attributes were determined using a priority matrix approach. Historic admissions data, comprising applicants' rural origin (defined only by location of high school graduation), composite scores, and ranking, were reanalyzed to identify the magnitude of numerical constants that, when applied to composite scores, enhanced the relative ranking of eligible rural-origin applicants. This resulted in a hypothetical 29%-33% increase in the number of rural-origin students in incoming classes in those years. In the inaugural year of implementation of the policy and methodology, 60 admission offers (44.1%) were made to applicants with one or more rural attributes. Without adjustments, only 49 applicants with rural attributes (36%) would have been offered admission. This methodology resulted in a 22.4% increase in admission offers to applicants with rural attributes, and ushered in an incoming class that was more representative of the province's rural-urban demographics than in previous years. This methodology, although focused on rurality, could be equally applicable to any attribute, and to achieve greater diversity and equity among medical school applicants.
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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 | 0.024 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 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".