Co-Developed Indigenous Educational Materials for Chronic Kidney Disease: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: Canadian Indigenous populations experience significantly more chronic kidney disease (CKD) than the general population. Indigenous people who live in rural and remote areas may also have difficulty accessing both information and care for their CKD. Informed decision making about treatment options for advancing kidney disease may be delayed, which can result in poor health outcomes and decreased quality of life. Moreover, Indigenous people may experience marginalization within Western health care systems. OBJECTIVE: The objective of this scoping review is to identify culturally appropriate and co-developed Indigenous educational tools that will ultimately support CKD learning and end-stage kidney treatment decision making. DESIGN: Scoping Review. SETTING: Databases included Embase, CINAHL, Medline (OVID), ERIC, and the Canadian Agency for Drugs and Technology Gray Matters. STUDY PARTICIPANTS: Community-based Indigenous patients, families, health care workers, and community members. METHODS: We systematically reviewed the literature to explore the availability of co-developed Indigenous educational tools and material for CKD treatment options. Titles, abstracts, and full texts were reviewed independently by 2 reviewers with disagreements resolved through a third. All aspects of this project, including searching the databases were done in consultation with an Indigenous Elder. RESULTS: Only one retrieved article identified a comprehensive CKD tool co-developed by researchers, health care providers, and an Indigenous community. Three themes emerged from the scoping review that may inform characteristics of co-developed tools: cultural appropriateness; appraisal of utility and effectiveness and; content informed by co-development of traditional and Western chronic disease knowledge. LIMITATIONS: Consistent with scoping review methodology, the methodological quality of included studies was not assessed. In addition, it was difficult to synthesize the findings from the research and gray literature. CONCLUSION: Little is known about the co-development of Indigenous educational tools for CKD. Further in-depth understanding is required about how to best engage with Indigenous communities, specifically to co-develop contextualized CKD tools that are acceptable to Indigenous people.Trial registration: Not applicable as this review described secondary data.
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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 | 0.042 | 0.137 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.021 | 0.026 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".