Using community-to-care provider dialogue to promote anti-racism and culturally safe health care
Bibliographic record
Abstract
Culturally safe approaches to health care are fundamental to addressing Indigenous-specific racism and improving health care experiences and health outcomes for Indigenous peoples. However, historical and structural barriers in British Columbia, Canada mean that health care and services are not consistently or predictably provided in a culturally safe manner as people cross from Indigenous-led health services to mainstream health care providers who serve Indigenous communities. The Nuu-chah-nulth Patient Voices Project was a participatory research-to-action project which aimed to facilitate dialogue between health care providers and Nuu-chah-nulth community members about the impact of culturally unsafe care delivery on health outcomes. The Nuu-chah-nulth Voices project was conducted in partnership with the Nuu-chah-nulth Tribal Council, Tseshaht First Nation and Mowachaht/Muchalaht First Nation. The research team utilized a storywork and brokered dialogue methodology facilitated between members of the participating First Nations and mainstream health care providers serving Nuu-chah-nulth communities, including family practice and emergency department physicians and nurse practitioners. The findings demonstrate that cultural safety requires a multi-pronged approach – including education, changes in standard care practices, policy and procedure changes in patient advocates and complaint processes, increasing access to primary care in Indigenous communities, and instigating workplace culture change in hospitals and clinics. Importantly, these strategies will be most effective when they are rooted in the perspectives of Indigenous patients and developed in collaboration with health care providers, bridging the divide between care provider and patient experiences.
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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 | 0.022 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.012 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.004 | 0.007 |
| 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".