Reported sources of health inequities in Indigenous Peoples with chronic kidney disease: a systematic review of quantitative studies
Bibliographic record
Abstract
BACKGROUND: To summarise the evidentiary basis related to causes of inequities in chronic kidney disease among Indigenous Peoples. METHODS: We conducted a Kaupapa Māori meta-synthesis evaluating the epidemiology of chronic kidney diseases in Indigenous Peoples. Systematic searching of MEDLINE, Google Scholar, OVID Nursing, CENTRAL and Embase was conducted to 31 December 2019. Eligible studies were quantitative analyses (case series, case-control, cross-sectional or cohort study) including the following Indigenous Peoples: Māori, Aboriginal and Torres Strait Islander, Métis, First Nations Peoples of Canada, First Nations Peoples of the United States of America, Native Hawaiian and Indigenous Peoples of Taiwan. In the first cycle of coding, a descriptive synthesis of the study research aims, methods and outcomes was used to categorise findings inductively based on similarity in meaning using the David R Williams framework headings and subheadings. In the second cycle of analysis, the numbers of studies contributing to each category were summarised by frequency analysis. Completeness of reporting related to health research involving Indigenous Peoples was evaluated using the CONSIDER checklist. RESULTS: Four thousand three hundred seventy-two unique study reports were screened and 180 studies proved eligible. The key finding was that epidemiological investigators most frequently reported biological processes of chronic kidney disease, particularly type 2 diabetes and cardiovascular disease as the principal causes of inequities in the burden of chronic kidney disease for colonised Indigenous Peoples. Social and basic causes of unequal health including the influences of economic, political and legal structures on chronic kidney disease burden were infrequently reported or absent in existing literature. CONCLUSIONS: In this systematic review with meta-synthesis, a Kaupapa Māori methodology and the David R Williams framework was used to evaluate reported causes of health differences in chronic kidney disease in Indigenous Peoples. Current epidemiological practice is focussed on biological processes and surface causes of inequity, with limited reporting of the basic and social causes of disparities such as racism, economic and political/legal structures and socioeconomic status as sources of inequities.
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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 | 0.074 | 0.207 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.012 |
| Bibliometrics | 0.030 | 0.024 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".