Equity, Diversity, and Inclusion in admissions: a critical qualitative inquiry on faculty leaders’ perspectives on barriers and facilitators at a Canadian health sciences institution
Bibliographic record
Abstract
Background: There is an ongoing need for Equity, Diversity, and Inclusion (EDI)-focused admission reform in Canadian health sciences programs. Extensive literature on critical race Theory (CRT) and Postcolonial Theory (PCT) have provided frameworks to understand and challenge existing inequities. However, there is a lack of research regarding specific challenges and dynamics involved in the application of CRT and PCT to admissions in health professions education. Methods: This study investigates systemic factors influencing EDI-focused admission reform through the perceptions of Canadian health sciences faculty leaders. Using a critical constructivist lens informed by CRT and PCT, we conducted semi-structured interviews with six leaders and applied critical thematic analysis, which uses theories of racism, coloniality, and power, to interpretate participants' views and institutional discourses. Results: Participants acknowledged bias in traditional admission metrics (e.g., GPA, MCAT) but continued to prefer them over equity-based alternatives, perceiving the former as better indicators of curricular and professional success. Admission reform was perceived to be a resource-intensive add-on that was difficult to prioritize. Broader societal and institutional forces, such as accreditation, peer institutions, and leadership discourses shaped support for equity initiatives. Conclusion: We conclude that the concurrent reliance on traditional measures of merit in admissions, curriculum, and practice reinforces the cultural currency of those colonial measures. Admission reform efforts should be accompanied by parallel initiatives across other academic domains and appropriate funding and regulatory support to break the self-fulfilling cycle of bias and inequity.
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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 | 0.022 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.029 | 0.025 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| 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".