Enhancing health care equity with Indigenous populations: evidence-based strategies from an ethnographic study
Bibliographic record
Abstract
BACKGROUND: Structural violence shapes the health of Indigenous peoples globally, and is deeply embedded in history, individual and institutional racism, and inequitable social policies and practices. Many Indigenous communities have flourished, however, the impact of colonialism continues to have profound health effects for Indigenous peoples in Canada and internationally. Despite increasing evidence of health status inequities affecting Indigenous populations, health services often fail to address health and social inequities as routine aspects of health care delivery. In this paper, we discuss an evidence-based framework and specific strategies for promoting health care equity for Indigenous populations. METHODS: Using an ethnographic design and mixed methods, this study was conducted at two Urban Aboriginal Health Centres located in two inner cities in Canada, which serve a combined patient population of 5,500. Data collection included in-depth interviews with a total of 114 patients and staff (n = 73 patients; n = 41 staff), and over 900 h of participant observation focused on staff members' interactions and patterns of relating with patients. RESULTS: Four key dimensions of equity-oriented health services are foundational to supporting the health and well-being of Indigenous peoples: inequity-responsive care, culturally safe care, trauma- and violence-informed care, and contextually tailored care. Partnerships with Indigenous leaders, agencies, and communities are required to operationalize and tailor these key dimensions to local contexts. We discuss 10 strategies that intersect to optimize effectiveness of health care services for Indigenous peoples, and provide examples of how they can be implemented in a variety of health care settings. CONCLUSIONS: While the key dimensions of equity-oriented care and 10 strategies may be most optimally operationalized in the context of interdisciplinary teamwork, they also serve as health equity guidelines for organizations and providers working in various settings, including individual primary care practices. These strategies provide a basis for organizational-level interventions to promote the provision of more equitable, responsive, and respectful PHC services for Indigenous populations. Given the similarities in colonizing processes and Indigenous peoples' experiences of such processes in many countries, these strategies have international applicability.
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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 | 0.091 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".