Barriers to Familial Consent in Deceased Organ Donation among Racialized and Indigenous Communities in Canada: A Qualitative Study
Bibliographic record
Abstract
Background: In Canada, over 3700 people are on the organ transplant list, with deceased donor kidney transplants making the majority of transplants completed annually. Despite the increasing numbers of transplants, populations marginalized by race and ethnicity have lower rates of organ donation registration and are less likely to consent to donation. Gaining insight into barriers to providing consent is critical in developing strategies to address disparities. This study aimed to identify barriers to familial consent among members of racialized and Indigenous communities. Methods: 48 participants were recruited through community-based organizations in British Columbia (BC) and included BC residents, aged over 19, who spoke English. 31 participants completed interviews and 17 completed focus groups. Participants were oversampled for members of racialized and Indigenous communities. A case vignette was used to collect data with data analyzed using summative content analysis. Results: Four overarching barriers were identified: 1) system-level; 2) community-based; 3) related to decision-making; and 4) informational. System-level barriers highlighted mistrust of Canadian healthcare institutions, perceived coercion, and the role of language in consent. Community-based barriers involved ideas around the deceased body, funeral, afterlife, and general perceptions of organ donation. Decision-making was affected by family dynamics and donor and recipient identity. Informational barriers such as age eligibility also influenced consent. Facilitators to address barriers include culturally diverse resources, increasing community knowledge, and providing language, cultural, and religious support to build trust and facilitate discussions. Conclusion: This study highlights barriers and modifiable determinants to familial consent in deceased organ donation among members of racialized and Indigenous communities. Although it examines barriers to familial consent for all organ donation, findings are of significant relevance to kidney care, as patients waiting for kidney transplants constitute the majority of patients on transplant waitlists. Education and engagement initiatives must be targeted at the health system and community levels to fully address barriers to consent and reduce racial and ethnic disparities in organ 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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.028 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".