Advancing gender inclusivity for <scp>Two‐Spirit</scp>, trans, nonbinary and other gender‐diverse blood and plasma donors
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Two-Spirit, trans, nonbinary and other gender-diverse (2STGD) donors face challenges in donation. While many blood operators aim to address these challenges, to date, no empirical study with these donors has been conducted to guide their efforts. This paper reports 2STGD donors' views on a two-step approach asking donors their gender and sex assigned at birth (SAAB), and expanding gender options in donor registration. MATERIALS AND METHODS: A qualitative community-based study was conducted with 2STGD donors (n = 85) in Canada. Semi-structured, in-depth interviews were conducted from July to October 2022, audio-recorded and transcribed. Data were analysed using a thematic analytic framework. RESULTS: Participants were divided on their views of a two-step approach asking gender and SAAB. Themes underlying views in favour of this approach included the following: demonstrating validation and visibility, and treating 2STGD donors and cisgender donors alike. Themes underlying views not in favour or uncertain included potential for harm, compromising physical safety, and invalidation. All participants were in favour of expanding gender options if blood operators must know donors' gender. CONCLUSION: Results indicate that a two-step approach for all donors is not recommended unless the blood operator must know both a donor's gender and SAAB to ensure donor and/or recipient safety. Gender options should be expanded beyond binary options. Ongoing research and evidence synthesis are needed to determine how best to apply donor safety measures to nonbinary donors.
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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 | 0.022 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".