Navigating Indigenous Representation in Medical School: The Path to Reconciliation
Bibliographic record
Abstract
To the Editor: Being in medical school in general can be difficult, but considering the added stressors of being Indigenous in a field where racism and discrimination against Indigenous people is often inescapable, one can see that being an Indigenous medical student comes with unique challenges.1,2 How can Indigenous medical students feel confident and understood after acceptance into medical school? Three challenges are present in navigating medical school as an Indigenous person: (1) medical curricula have only in recent years started offering streams focused on Indigenous people’s health1,2; (2) many non-Indigenous medical students may experience discomfort in acknowledging their own privilege and prefer not to do so3; and (3) Indigenous medical students often have survivors of Indian Residential Schools in their families, making medical school feel that much more institutional. To openly advocate for changes as an Indigenous student is worthy of applause, as they often have difficulty just comprehending their own existence in an institution fraught with nepotism and privilege. Therefore, Indigenous students can benefit immensely from having an Indigenous physician mentor build their confidence in medical school. Because of my mentor, I have learned that graduating from medical school as an Indigenous mother is doable, an experience that my mentor and I share. Many medical students have parents or close family members who are physicians, and so they often receive an advantage in understanding how to navigate medical school. Without my mentor, I fear that I would have had no one to guide me during these years, especially when considering the added experience of being Indigenous. It is my hope that medical schools across the country welcome the opportunity to have Indigenous physicians mentoring Indigenous medical students. This will not only provide practical and emotional support for Indigenous medical students but also strengthen them to celebrate their identity and lead health care further into truth and reconciliation. In this way, Indigenous medical students will feel more welcome in medical schools with help from those who have walked a mile in their moccasins. Elycia E. Monaghan Medical student, Northern Ontario School of Medicine University, Thunder Bay, Ontario, Canada; email: [email protected]
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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 | 0.006 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.020 | 0.034 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".