Gidoodikosan Giiwitaabimin - We sit in a circle for kidneys, a strength-based qualitative study.
Bibliographic record
Abstract
Abstract The overall aim of this qualitative study was to inform recommendations towards Indigenous strength-based approaches for kidney health, prevention of disease and failure. Between 2020 and 2022, during the COVID-19 pandemic, we conducted online interviews and teleconferences with 16 participants from North Simcoe in Ontario, Canada. Participants shared their experiences revolving around kidney disease and care within Indigenous contexts. An advisory committee consisting of members from a First Nation community with personal and familial experiences with kidney disease provided direction to the research in conjunction with the research team. Analysis occurred through the collective consensual data analytic procedure (CCDAP) consistent with a strength-based methodology that involved the coming together of community members with the research team. Themes were identified into groupings that led to the recommendations related to (1) health care continuity of traditional and cultural ways of knowing and being; (2) accessible hemodialysis and support for home dialysis; (3) increased kidney transplantation and kidney organ donation, (4) increased telehealth and virtual medicine; and (5) government support and funding. Accessible culturally safe care within home communities that also serves to limit the spread of COVID-19 infection was prioritized.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".