Educating for Indigenous Health Equity: An International Consensus Statement
Bibliographic record
Abstract
The determinants of health inequities between Indigenous and non-Indigenous populations include factors amenable to medical education's influence-for example, the competence of the medical workforce to provide effective and equitable care to Indigenous populations. Medical education institutions have an important role to play in eliminating these inequities. However, there is evidence that medical education is not adequately fulfilling this role and, in fact, may be complicit in perpetuating inequities.This article seeks to examine the factors underpinning medical education's role in Indigenous health inequity, to inform interventions to address these factors. The authors developed a consensus statement that synthesizes evidence from research, evaluation, and the collective experience of an international research collaboration including experts in Indigenous medical education. The statement describes foundational processes that limit Indigenous health development in medical education and articulates key principles that can be applied at multiple levels to advance Indigenous health equity.The authors recognize colonization, racism, and privilege as fundamental determinants of Indigenous health that are also deeply embedded in Western medical education. To contribute effectively to Indigenous health development, medical education institutions must engage in decolonization processes and address racism and privilege at curricular and institutional levels. Indigenous health curricula must be formalized and comprehensive, and must be consistently reinforced in all educational environments. Institutions' responsibilities extend to advocacy for health system and broader societal reform to reduce and eliminate health inequities. These activities must be adequately resourced and underpinned by investment in infrastructure and Indigenous leadership.
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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 | 0.121 | 0.090 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.009 | 0.018 |
| Research integrity | 0.029 | 0.035 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".