Truth and Reconciliation in Medical Schools: Forging a Critical Reflective Framework to Advance Indigenous Health Equity
Bibliographic record
Abstract
In 2015, the Truth and Reconciliation Commission (TRC) of Canada outlined 94 Calls to Action, which formalized a responsibility for all people and institutions in Canada to confront and craft paths to remedy the legacy of the country's colonial past. Among other things, these Calls to Action challenge medical schools to examine and improve existing strategies and capacities for improving Indigenous health outcomes within the areas of education, research, and clinical service. This article outlines efforts by stakeholders at one medical school to mobilize their institution to address the TRC's Calls to Action via the Indigenous Health Dialogue (IHD). The IHD used a critical collaborative consensus-building process, which employed decolonizing, antiracist, and Indigenous methodologies, offering insights for academic and nonacademic entities alike on how they might begin to address the TRC's Calls to Action. Through this process, a critical reflective framework of domains, reconciliatory themes, truths, and action themes was developed, which highlights key areas in which to develop Indigenous health within the medical school to address health inequities faced by Indigenous peoples in Canada. Education, research, and health service innovation were identified as domains of responsibility, while recognizing Indigenous health as a distinct discipline and promoting and supporting Indigenous inclusion were identified as domains within leadership in transformation. Insights are provided for the medical school, including that dispossession from land lays at the heart of Indigenous health inequities, requiring decolonizing approaches to population health, and that Indigenous health is a discipline of its own, requiring a specific knowledge base, skills, and resources for overcoming inequities.
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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 | 0.178 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.042 | 0.165 |
| Scholarly communication | 0.034 | 0.030 |
| Open science | 0.007 | 0.037 |
| Research integrity | 0.012 | 0.028 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".