Facing hard truths: Medical education's reckoning with settler colonialism in an era of reconciliation
Bibliographic record
Abstract
INTRODUCTION: Medical schools are responsible for embedding Indigenous health education across the training continuum. Central to this work is recognising settler colonialism as an ongoing structure that privileges non-Indigenous peoples while producing and sustaining inequities for Indigenous communities. This paper explores key learning moments as non-Indigenous medical learners and faculty reflect on their experiences within systems that promote reconciliation yet remain largely rooted in colonial logic. METHODS: Data collection and analysis were informed by the principles of narrative inquiry. Five non-Indigenous medical students, a health research graduate student and 10 medical educators (MD and PhD) consented to participate in a narrative interview about how they positioned themselves and supported others engagement in ongoing reconciliatory efforts within their institution. Data were gathered over 2020-2022. RESULTS: Participants acknowledged their privileged position and aimed to leverage it to address educational or health disparities affecting Indigenous peoples. Yet intervening when they witnessed unfairness proved challenging. Although many attempted to adopt a proactive stance and advocate for systemic change, the prevailing tendency in such situations was to avoid disrupting the status quo due to perceived gaps in their knowledge or apprehension about professional backlash. CONCLUSION: Non-Indigenous medical learners and faculty struggle to navigate a system calling for transformation yet rife with historical and institutional barriers. This struggle often arises from the discomfort stemming from their privilege and a sense of limited influence within the medical hierarchy.
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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 | 0.017 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.060 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 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".