“I Just Don't Trust It”: Exploring the Role of Mistrust in Shaping Living Donor Kidney Transplant Pathways for African, Caribbean, and Black communities in Toronto, Canada
Bibliographic record
Abstract
Background: In Canada, African, Caribbean, and Black [ACB] patients with kidney failure are 40-70% less likely to receive a LDKT compared to Whites. To date, research has focused on individual factors, neglecting the impact of systemic racism in shaping ACB community attitudes toward LDKT. Further absent is Canadian research using qualitative methodologies. Methods: We used an exploratory qualitative approach to understand perspectives and attitudes about LDKT in Canadian ACB communities. Using purposive and snowball sampling, we recruited 81 self-identified ACB community participants to take part in eight focus group discussions between January and November 2020. Participants were asked questions about their racial and ethnic identities, medical experiences and attitudes, and knowledge and perspectives on LDKT. We then applied a Critical Race analytical framework to analyze transcripts, focusing on the tenets of racial consciousness, social location, power dynamics, and counternarratives. Results: Of the 81 participants 63% was female, 46% were <50 years of age, 53% were immigrants to Canada. 36% self-identified as North American Black/African; 48% as Caribbean; 6% as North African; and 4% as Central/West African. Three key themes emerged from the data. First, we found that like in the U.S., participants expressed medical mistrust. Second, this medical mistrust was rooted in a combination of processes of racialization (medical racism), historical legacies of medical mistreatment, and lived negative experiences within the health care system. Lastly, medical mistrust informed health and illness related decision-making risk assessment, perspectives on LDKT, (lack of) engagement with traditional health care settings, and medical needs. Conclusions: ACB community attitudes and decision-making processes about LDKT are complex, historically-rooted, and informed by broader medical mistrust. This suggests that broader systemic racial barriers to adequate health care outside of the LDKT pathway, may have far-reaching effects. Further research is needed to better understand how the broader medical experiences of ACB communities may be an underappreciated factor that shapes racial disparities in transplantation, and the kinds of interventions needed to facilitate better access to LDKT. Funding: Government Support - Non-U.S.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.033 | 0.013 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 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".