Inequities in organ and tissue donation and transplantation for sexual orientation and gender identity diverse people: A scoping review
Bibliographic record
Abstract
Sexual orientation and gender identity (SOGI)-diverse populations experience discrimination in organ and tissue donation and transplantation (OTDT) systems globally. We assembled a multidisciplinary group of clinical experts as well as SOGI-diverse patient and public partners and conducted a scoping review including citations on the experiences of SOGI-diverse persons in OTDT systems globally to identify and explore the inequities that exist with regards to living and deceased OTDT. Using scoping review methods, we conducted a systematic literature search of relevant electronic databases from 1970 to 2021 including a grey literature search. We identified and screened 2402 references and included 87 unique publications. Two researchers independently coded data in included publications in duplicate. We conducted a best-fit framework synthesis paired with an inductive thematic analysis to identify synthesized benefits, harms, inequities, justification of inequities, recommendations to mitigate inequities, laws and regulations, as well as knowledge and implementation gaps regarding SOGI-diverse identities in OTDT systems. We identified numerous harms and inequities for SOGI-diverse populations in OTDT systems. There were no published benefits of SOGI-diverse identities in OTDT systems. We summarized recommendations for the promotion of equity for SOGI-diverse populations and identified gaps that can serve as targets for action moving forward.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".