Strategies For Enhancing Equity, Diversity, and Inclusion in Medical School Admissions–A Canadian Medical School's Journey
Bibliographic record
Abstract
Background Medical schools aim to select and train future physicians representative of and able to serve their diverse population needs. Enhancing equity, diversity, and inclusion (EDI) in admissions processes includes identifying and mitigating barriers for those underrepresented in medicine (URM). Summary of Innovations In 2017, Schulich School of Medicine and Dentistry (Western University, Ontario, Canada) critically reviewed its general Admissions pathways for the Doctor of Medicine (MD) program. Till that time, interview invitations were primarily based on academic metrics rather than a holistic review as for its Indigenous MD Admissions pathway. To help diversify the Canadian physician workforce, Schulich Medicine utilized a multipronged approach with five key changes implemented over 2 years into the general MD Admissions pathways: 1. A voluntary applicant diversity survey (race, socioeconomic status, and community size) to examine potential barriers within the Admissions process; 2. Diversification of the admissions committee and evaluator pool with the inclusion of an Equity Representative on the admissions committee; 3. A biosketch for applicants' life experiences; 4. Implicit bias awareness training for Committee members, file reviewers and interviewers; and 5. A specific pathway for applicants with financial, sociocultural, and medical barriers (termed ACCESS pathway). Diversity data before (Class of 2022) vs. after (Class of 2024) these initiatives and of the applicant pool vs. admitted class were examined. Conclusion For the Class of 2024, the percentage of admitted racialized students (55.2%), those with socioeconomic challenges (32.3%), and those from remote/rural/small town communities (18.6%) reflected applicant pool demographics (52.8, 29.9, and 17.2%, respectively). Additionally, 5.3% (vs. 5.6% applicant pool) of admitted students had applied through ACCESS. These data suggest that barriers within the admissions process for these URM populations were potentially mitigated by these initiatives. The initiatives broadly improved representation of racialized students, LGBTQ2S+, and those with disability with statistically significant increases in representation of those with socioeconomic challenges (32.3 vs. 19.3%, p = 0.04), and those with language diversity (42.1 vs. 35.0%, p = 0.04). Thus, these changes within the general MD admissions pathways will help diversify the future Canadian physician workforce and inform future initiatives to address health equity and social accountability within Canada.
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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 | 0.049 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.044 | 0.012 |
| Scholarly communication | 0.017 | 0.005 |
| Open science | 0.006 | 0.025 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".