Using community data to inform medical school admissions: a cohort study of medical students in Ottawa, Canada
Bibliographic record
Abstract
Background: To advance social accountability in our medical school admissions, this study aims to examine how sociodemographic profiles of students admitted to undergraduate medicine at the University of Ottawa compare to those of the Ottawa regional community. Methods: Weconducted a cohort study of our 2023 first-year MD students. We used data from the Ontario Medical Student Applicant Service and the Ottawa Neighbourhood Study to descriptively compare nine sociodemographic factors. Results: Of 183 students, our cohort demonstrated greater diversity in non-official first languages, second and third-generation status, and non-White racial identities. However, Black students (4.4% vs. 6.3%) were underrepresented, and Indigenous students (4.4% vs. 3.2%) were likely underrepresented given the known underreporting of Indigenous identity in census data. Students from the highest-earning households (32.8% vs. 13.2%), and with parents working in education (30.1% vs 16.5%) or health (15.8% vs 7.5%) professions were overrepresented. Conclusions: This study demonstrates student underrepresentation for some sociodemographic factors and serves as an approach for other medical schools to consider admissions representation using local data.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".