“Don't shut down, these conversations need to happen”: Indigenous health professionals insights for advancing anti‐racism in health care
Bibliographic record
Abstract
BACKGROUND: Indigenous peoples around the world continue to experience systemic racism and discrimination within health care, as a direct consequence of colonisation. In settler-colonial states, such as Canada, current approaches to tackling anti-Indigenous racism are often designed by non-Indigenous peoples. Combating racism necessitates that health care policies and practices be co-constructed with Indigenous communities. OBJECTIVE: This study explored insights from Indigenous health professionals, educators and community members. It aimed to identify pathways for justice and equity-based medical curricular reform that, while being Indigenous-led, also engage practitioners in institutional accountability. METHODS: Semi-structured interviews were conducted with 12 Indigenous individuals with extensive experiential, professional and academic experience with the health care system and health professions education. RESULTS: This study highlights the ongoing impacts of anti-Indigenous racism in medical education and health care settings. Indigenous-specific racism within medicine manifests through the dehumanisation of Indigenous peoples, deficit-based approaches to Indigenous health education, and the erasure, omission, or other types of violence and epistemic injustices in educational settings and curricula design. Indigenous approaches to addressing it pivot around sovereignty and self-determination. These include nurturing the Indigenous principle of relationality within institutions, policies, education and interactions; challenging dehumanising narratives by centring Indigenous voices; and re-humanising medical practice through skills that foster connectedness and by embedding justice and equity as core tenets of medical practice. CONCLUSION: Indigenous knowledge, principles and insights offer promising approaches for paving the way towards equity- and justice-centred medical practice and education. This study underscores the need to centre Indigenous voices, incorporate Indigenous knowledge and meaningfully engage with communities to embed health equity and justice at the core of medical education and practice.
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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 | 0.031 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.039 | 0.042 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.007 | 0.019 |
| Insufficient payload (model declined to judge) | 0.006 | 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".