Chronic kidney disease among Indigenous populations: considerations for effective and ethical research
Bibliographic record
Abstract
Chronic kidney disease (CKD) is a well-documented and growing problem among Indigenous populations in North America and Australia. Further, urgent research is needed to develop appropriate interventions to slow development and progression of CKD and to improve outcomes in Indigenous* communities affected by the burden of kidney disease. For effective research to occur, researchers need to develop and maintain a multifaceted and collaborative approach to working with Indigenous research subjects and their communities. We review two fundamental concepts or paradigms which may cause misinformation or confusion in conducting health research in Indigenous populations. First, we examine systems of health knowledge and discuss the divergences between investigator and Indigenous perspectives, and how they interface in a research context. Secondly, we review the concept of research methods for Indigenous populations, to highlight ways to develop a collaborative and culturally inclusive health research process.
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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 | 0.604 | 0.519 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.026 | 0.111 |
| Scholarly communication | 0.031 | 0.036 |
| Open science | 0.010 | 0.027 |
| Research integrity | 0.035 | 0.043 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".