Barriers and Enablers to Implementing EDI-focused Admissions Initiatives at Queen’s Health Sciences
Bibliographic record
Abstract
Members of equity-deserving groups face substantial barriers in matriculating into health professional programs. Equity-focused admissions interventions such as fee assistance programs, holistic review, diverse reviewer panel, and bias training could be used to address these barriers as suggested in the literature. Despite the stated commitment of multiple Canadian institutions towards improving equity in admissions, equity-focused admissions interventions are currently scarcely implemented. Queen’s Health Sciences (QHS), led by Dean Jane Philpott, has created an Action Table to revise and enhance the implementation of equity-focused admissions. In our study, we explored the perspectives of decanal members regarding barriers and enablers to implementing equity-focused admissions initiatives within QHS schools and programs. We conducted semi-structured interviews with six leaders across QHS and analyzed interview data using a constructivist approach. Barriers to implementing equity-focused admissions initiatives include concerns about matriculants' quality; a lack of resources for implementation; and a homogenous faculty, student, and applicants pool. Enablers include extrinsic motivators for participation in equity work, existing buy-in from members of QHS, increasing emphasis on equity in other areas of academic programs, other institutions, and society. By influencing students, faculty, and staff's values, beliefs, and actions, meritocratic culture may be creating the barriers identified in our study and reinforcing admissions processes that disadvantage certain groups. On the other hand, enablers may be intentionally disrupting the reinforcement cycle by encouraging beliefs and behaviour change. More research on the influence of societal and academic culture on equity in admissions and equity interventions’ influence on culture is needed.
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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 | 0.010 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".