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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".