Understanding systemic barriers to blood donation for people of South Asian ancestry: A qualitative study
Bibliographic record
Abstract
We explored how South Asian communities in the Greater Toronto Area (GTA) understand and experience blood donation, systemic barriers to donation, and offer recommendations for blood operators to foster inclusivity and equity in blood donation systems. Using a community-based participatory research approach, we conducted semi-structured interviews with 10 community leaders of South Asian ancestry. Reflexive thematic analysis was used to identify themes related to identity, belonging, and barriers to blood donation, and these themes were understood through the lens of transnationalism. Participants demonstrated the complexity of identity and belonging, and the responsibility of balancing cultural ties with integration into Canada. They faced systemic challenges, including settlement, economic and social challenges, racism and discrimination, and demonstrated resilience in balancing personal and community needs and responsibilities. Blood donation served as a personal commitment and an act of service to society. Key motivations to donate included service to humanity, upholding cultural and spiritual values, and reciprocity. Key barriers included accessibility, language barriers, deferral policies, and lack of tailored outreach and education strategies. Despite these barriers, participants demonstrated their commitment to donation and increasing participation in Canada's blood donation system. Blood operators can work towards addressing systemic barriers to donation by building relationships with community leaders, adopting culturally sensitive outreach strategies, and improving accessibility. Although limited to community leaders in the GTA, this study provides meaningful insights into the challenges faced by South Asian communities. Further research involving other underrepresented demographics can inform efforts to enhance participation, inclusivity, and equity in blood donation systems.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| 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".