Inequities in the Care and Outcomes of Indigenous People Living With Kidney Failure
Bibliographic record
Abstract
Kidney failure, defined as end-stage kidney disease requiring kidney replacement therapy, is a public health concern that disproportionately affects indigenous peoples around the world. Despite advancements in medical technologies and preventive public health interventions in kidney care, indigenous peoples continue to face significant barriers that limit their access to care. These barriers include limited availability of kidney care services in remote areas, cultural and language obstacles, systemic racism, low health literacy, geographic isolation, and mistrust in health care systems. Such challenges contribute to notable disparities in kidney disease outcomes and kidney replacement therapy access. Previous research in countries such as Canada, Australia, New Zealand, and the United States demonstrates a disproportionate burden of risk factors, chronic kidney disease, and the consequences of kidney failure and other complications among indigenous peoples. In this review, we explore the global landscape of kidney failure among indigenous populations, examining epidemiological data, barriers to care, and outcomes of kidney replacement therapy. The key objective is to provide a comprehensive overview of disparities in the burden of kidney failure and care inequities experienced by this high-risk population group. We propose culturally sensitive, community-driven solutions to mitigate various inequities. Addressing these issues requires acknowledging and overcoming the unique challenges faced by indigenous communities, including enhancing access to home dialysis and transplantation services and implementing culturally appropriate health care practices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".