Organ and tissue donation and transplantation in people who identify as Two-Spirit, gay, bisexual, transgender, queer, intersex, and more: a Canada-wide cross-sectional community survey
Bibliographic record
Abstract
PURPOSE: Inequities for sexual- and gender-minoritized (SGM) populations in organ and tissue donation and transplantation (OTDT) have been identified. We aimed to understand the awareness and attitudes of community members who are SGM regarding current SGM-relevant policies, policy gaps, and policy alternatives in Canadian OTDT systems. METHODS: We conducted an online, cross-sectional, Canada-wide survey of members of the Two-Spirit, gay, bisexual, transgender, queer, intersex and more (2SGBTQI+) communities (June-September 2024). Questionnaire development adhered to current survey science recommendations. We present descriptive data as counts and proportions. RESULTS: We analyzed responses from a convenience sample of 2,276 participants from across Canada. Most participants self-identified as White cisgender gay men. Prior to questionnaire completion, only 17% (257/1,497) of participants were aware that men who have sex with men are considered "increased infectious risk donors" in OTDT and cannot donate tissues, and 20% (302/1,512) were aware that they may only donate organs if the benefit to the recipient is deemed to outweigh the risk. Of the respondents, 18% (270/1,535) did not realize that potential donors are screened on the basis of sex assigned at birth as opposed to self-identified gender identity. On average, 23% (1,406/6,084) of participants perceived these policies as nondiscriminatory. Of the respondents, 60% (1,865/3,115) supported gender-neutral, behavior-focused donor risk assessments that were data-driven. Most participants reported a willingness to donate organs (79%; 1,237/2,276) and tissues (72%; 1,123/2,276) after death. CONCLUSIONS: People in Canada who self-identify as 2SGBTQI+ perceived current OTDT policies to be discriminatory and in need of equitable revision. Evidence-based behavioral and gender-neutral donor risk criteria were preferred over identity-based criteria. The majority of survey participants who identify as SGM were willing to donate organs and tissues despite current system inequities. Health Canada should revise current donor risk assessment criteria to ensure they are data-driven and optimize access and safety to OTDT in Canada.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".