Addressing Disparities in Kidney Health Outcomes for First Nations Peoples of the United States, Canada, Australia, and New Zealand: A Systematic Review
Bibliographic record
Abstract
Background: First Nations Peoples of colonised counties are disproportionately burdened with kidney failure. Current systems of kidney care are failing to meet their needs, despite strong advocacy from community. We aimed to identify how disparities in health outcomes for First Nations Peoples of colonial countries living with kidney failure are addressed through different models of care. Methods: We conducted a systematic review according to the PRISMA Checklist, governed by a First Nations reference group. Included studies involved First Nations Peoples of the USA, Canada, New Zealand and Australia and interventions to address the management or complications of kidney failure. The certainty of the evidence was assessed using GRADE. Results: We identified 31 studies across 5 domains: dialysis care, dialysis access (vascular/peritoneal), transplantation, kidney failure complications, nutrition, and cultural safety. Few First Nations-specific randomised trials were identified. The largest body of evidence came from Australia and related to community-based dialysis care. From the Americas there is a moderate level of evidence for co-created, community-based living kidney donor transplant education and awareness campaigns. Conclusions: Within the limited literature, there is evidence that purposeful, First Nations-led interventions can have positive impacts. However, considering the inequities faced by First Nations Peoples of colonial countries there is an unacceptable paucity of intervention studies evaluating First Nations specific models of kidney care. Funding: Government Support - Non-U.S.Figure 1:: Adapted PRISMA diagram of study selectionFigure 2:: Data analysis schema
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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 | 0.015 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.018 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".