Navigating ethics in HIV data and biomaterial management within Black, African, and Caribbean communities in Canada
Bibliographic record
Abstract
BACKGROUND: This study explored the ethical issues associated with community-based HIV testing among African, Caribbean, and Black (ACB) populations in Canada, focusing on their perceptions of consent, privacy, and the management of HIV-related data and bio-samples. METHODS: A qualitative community-based participatory research (CBPR) approach was employed to actively engage ACB community members in shaping the research process. The design included in-depth qualitative interviews with 33 ACB community members in Manitoba, Canada. The study was guided by a Community Guiding Circle, which contributed to study design, data analysis, and interpretation. A diverse sample was recruited through community agencies, social media, and flyers, with considerations for variations in age, gender, sexual orientation, and geographical location. The study employed iterative inductive thematic data analysis. FINDINGS: Participants expressed significant concerns about the collection, sharing, and use of HIV data from healthcare encounters, revealing mistrust towards institutions like police, child welfare, and immigration accessing their health information. Their worries centered on the handling of biological samples, data misuse, potential human rights violations, HIV criminalization, deportations, challenging consent, privacy, and bodily autonomy principles. While open to contributing to medical research, they unanimously demanded greater transparency, informed consent, and control over the secondary use of their health data. CONCLUSIONS: The study underscores the need for culturally safe approaches in HIV testing and ethical governance in healthcare for ACB communities. It highlights the importance of prioritizing participant empowerment, ensuring transparency, practicing informed consent, and implementing robust data security measures to balance effective HIV information management with the protection of individual rights.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.041 | 0.013 |
| Scholarly communication | 0.008 | 0.001 |
| Open science | 0.002 | 0.008 |
| 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".