Toward Understanding Culturally Sensitive Care for Transgender Blood Donors: A Scoping Review of Health Care Provider Knowledge
Bibliographic record
Abstract
Transgender is an umbrella term for individuals whose gender differs from the sex assigned at birth. Some transgender blood donors report distressing donation experiences, indicating possible challenges in providing culturally sensitive care to this group. Discourse regarding best practices for transgender blood donor care is absent in current literature. To begin to address this gap, a systematic scoping review applying Arksey and O'Malley's methodological framework was undertaken to identify studies involving health care providers (HCPs) and their knowledge and experiences delivering care to transgender patients. Eight studies of 256 eligible articles and studies met inclusion criteria. Thematic analysis revealed both health care system gaps and practice gaps. System gaps included rigid binary intake processes, uncertainty regarding how transgender individuals are identified in practice, and difficulties knowing when to ask about and use pronouns. Practice gaps identified a lack of education to assist in caring for transgender individuals, as evidenced by confusion with and conflating of terminology and pathologizing of transgender patients. Additionally, biases regarding the preference and prevalence of gender-affirming medical interventions and confusion regarding how and when to discuss these interventions with transgender patients were found. HCPs also exhibited a lack of understanding of how the health care system can be stigmatizing for transgender individuals and how this stigma can elevate patient health risks. Key knowledge gaps were identified and best practice recommendations were highlighted, which (if examined at blood centers) might improve provision of culturally sensitive care for transgender donors.
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 | 0.030 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.021 | 0.019 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| 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".