Experiential Evidence of Systemic Racism for Indigenous Peoples Navigating Transplantation in Canada
Bibliographic record
Abstract
Introduction: Despite previous data stating that Indigenous patients with kidney failure are 66% less likely to receive a kidney transplant compared with White Canadians, there is a very limited understanding of the barriers and challenges experienced and described by Indigenous Peoples when accessing kidney transplantation. The aim of this study was to describe the perspectives and experiences of Indigenous kidney transplant candidates and recipients, living kidney donors, and Elders on access to kidney transplantation in British Columbia, Canada in the hopes of codeveloping and implementing health services interventions to address systemic barriers to transplantation. Methods: = 37). Transcripts were thematically analyzed. Results: Five themes were identified as follows: (i) confronting uncertainty and risk, (ii) culture of giving, (iii) systemic racism and discrimination, (iv) navigating complexities of transplant and donation process, and (v) a lack of culturally safe care. Conclusion: These findings highlight that Indigenous patients face potentially modifiable barriers that may be amenable to health system improvements, such as development of culturally safe patient education tools and Indigenous-specific navigation supports. Health services and policy interventions need to be explored and evaluated to begin to address inequities in access to transplantation.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.012 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| 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".