Truth to Action: Lived Experiences of Indigenous Healthcare Professionals Redressing Indigenous-Specific Racism
Bibliographic record
Abstract
Study BackgroundThe experience of discrimination through stereotyping, profiling, and bias-informed care not only leads to poor access to healthcare services, but low retention rates of Indigenous health professionals (IHP). As health systems transformation evolves, a significant gap remains in supporting IHP to safely address racism, to be supported culturally to bring their authentic selves and voices to work, and to attend to one's own intellectual, physical, relational, cultural and spiritual wellness within a westernized model of care.PurposeThe aim of the study was to investigate the experiences of IHP working in mainstream healthcare in order to understand how their work environment impacts the delivery of cultural safe practices. What is reported in this manuscript, as an exercise in truth-telling, is findings about lived experiences of IHP working in one mainstream provincial healthcare region, and not the whole context and outcomes of the study.MethodsUsing Indigenous research methodologies, we embodied our Indigeneity into every facet of the research process. We facilitated three talking circles with participants grounded in a distinct cultural and ceremonial context following Secwepemc protocols.ResultsThe collective voices of IHP revealed the following common experiences: confronting genocide; addressing Indigenous-specific racism; uprooting toxicity and inequities; and upholding Indigenous human rights while enhancing accountability of systems transformation.ConclusionsThe experience of IHP working in health systems goes beyond mere individual employment obligations, its often about a families and communities advocacy for Indigenous rights, culturally safe working environments and access to dignified and respectful healthcare service. This study highlights the need for IHP to be actively involved in health system transformation to ensure the redesigning and restructuring of healthcare service delivery by and for Indigenous Peoples remains centered on Indigenous health and human rights.
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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 | 0.012 | 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 | 0.028 | 0.031 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".