Exploring Stigma in the Context of Living Donor Kidney Donor Transplantation (LDKT) Among African, Caribbean, and Black (ACB) Communities in Toronto, Canada
Bibliographic record
Abstract
Background: From a medical perspective, LDKT is the best treatment option for kidney failure. Patients from ACB communities are much less likely to receive LDKT than White patients. Stigma surrounding kidney failure and LDKT may contribute to this inequity. This qualitative analysis aims to understand the nature of stigma and how it may influence access to LDKT in ACB communities in Toronto, Canada. Methods: Self-identified ACB participants (individuals with [on dialysis or after kidney transplant] and without lived experience with kidney failure; and health care professionals [HCPs]) were recruited using purposive and snowball sampling via community networks and social media. Semi-structured in-depth individual interviews (IDIs) and focus groups (FGs) were conducted, audio-recorded, and transcribed verbatim. Reflexive Thematic Analysis was utilized, drawing on the tenets of Critical Race Theory (CRT) and Intersectionality to consider the effects of racialization and the interlocking effects of co-occurring social identities such as race, class, and health status and access to LDKT. Themes were developed, refined, and finalized by the research team. Results: The sample is comprised of 6 community FGs (n=81), 7 IDIs with HCPs, 9 patient IDIs, and 2 FGs (n=6) with patients with kidney failure. Participants expressed hesitancy around communicating about kidney disease and the need for LDKT due to anticipated and experienced stigma. Participants with kidney failure feared judgment from family, friends, and community (e.g. due to anticipated assumptions about lifestyle choices), as well as from HCPs (e.g. due to anticipated assumptions about health beliefs and behaviours). Participants also described a strong cultural norm of maintaining privacy around health issues, largely limiting any discussion about LDKT. Conclusions: Stigma is a potential barrier to LDKT as it may prevent discussions about kidney failure and treatment options and reduce the chance of identifying potential living donors. Culturally tailored, competent resources co-developed with ACB communities may help reduce stigma in ACB communities and may improve equitable 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.026 | 0.009 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".