An equity-oriented admissions model for Indigenous student recruitment in an undergraduate medical education program
Bibliographic record
Abstract
BACKGROUND: With the 2015 publication of the Truth and Reconciliation Commission of Canada's calls to action, health professional schools are left grappling with how to increase the recruitment and success of Indigenous learners. Efforts to diversify trainee pools have long looked to quota-based approaches to recruit students from underserved communities, though such approaches pose dilemmas around meaningfully dismantling structural barriers to health professional education. Lessons shared here from developing one multi-layered admissions strategy highlight the importance of equity-rather than equality-in any recruitment for learners from medically underserved communities. SUMMARY: The promotion of fairness in the recruitment of future practitioners is not just a question of equalizing access to, in this case, medical school; it involves recognizing the wider social and structural mechanisms that enable privileged access to the medical profession by members of dominant society. This recognition compels a shift in focus beyond merely giving the disadvantaged increased access to an unfair system, towards building tools to address deeper questions about what is meant by the kind of excellence expected of applicants, how it is to be measured, and to what extent these recruits may contribute to improved care for the communities from which they come. CONCLUSION: Equity-based approaches to student recruitment move health professional schools beyond the dilemma of recruiting students from marginalized backgrounds who happen to be most similar to the dominant student population. Achieving this requires a complex view of the target population, recognizing that disadvantage is experienced in many diverse ways, that barriers are encountered along a spectrum of access, and that equity may only emerge when a critically, socially conscious approach is embedded throughout institutional practices.
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 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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".