Bridging the Gaps and Mapping Strategies for Optimal Indigenous Kidney Health at Global Scale
Bibliographic record
Abstract
Background: Approximately 480M individuals constituting 6% of the global population are Indigenous Peoples. Despite the richness and diversity of their cultures, the shared legacy of colonization has profoundly influenced their health outcomes and socioeconomic status. Indigenous Peoples experience disproportionately high rates of CKD, yet often encounter substantial barriers in access to and quality of care. These challenges are further compounded by geographic isolation, inadequate healthcare infrastructure, environmental exposures, and socioeconomic disadvantage. Methods: Achieving ASN vision “A World Without Kidney Diseases” requires urgent attention to the needs of high-risk populations of Indigenous Peoples. We have reviewed the current status of Indigenous kidney health across regions (Africa, Asia, Australia, Canada, Latin America, New Zealand, the Pacific Islands, and US), highlighting the variability in CKD burden (Figure) and determinants, and outlining culturally safe and responsive strategies that improve care delivery and outcomes. Results: We are presenting epidemiological and contextual analyses to provide a comprehensive global overview of Indigenous kidney health, examining how intersecting factors: colonial histories, social determinants of health, and systemic exclusion—drive poor outcomes. In particular, we explored how limited access to early intervention, culturally competent care, and sustainable kidney replacement therapy options contributes to elevated morbidity and mortality. Conclusion: By promoting culturally responsive practices and addressing systemic barriers, this new information contributes to a broader understanding of how to bridge persistent gaps in care. It also aims to empower healthcare professionals to better support Indigenous communities through respectful, informed, and inclusive approaches to treatment and prevention. Funding: Government Support – Non-U.S.Global map summarizing the prevalence (%) of chronic kidney disease in Indigenous populations
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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 | 0.018 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".