Willingness of Older Canadians with HIV to Participate in HIV Cure Research Near and After the End of Life: A Mixed-Method Study
Bibliographic record
Abstract
HIV cure research requires interrogating latent HIV reservoirs in deep tissues, which necessitates autopsies to avoid risks to participants. An HIV autopsy biobank would facilitate this research, but such research raises ethical issues and requires participant engagement. This study explores the willingness to participate in HIV cure research at the end of life. Participants include Canadians with HIV [people with HIV (PWHIV)] aged 55 years or older. Following a mixed-method study design, all participants completed a phone or online survey, and a subset of participants participated in in-depth phone or videoconference interviews. We produced descriptive statistics of quantitative data and a thematic analysis of qualitative data. Barriers and facilitators were categorized under domains of the Theoretical Domains Framework. From April 2020 to August 2021, 37 participants completed the survey (mean age = 69.9 years old; mean duration of HIV infection = 28.5 years), including 15 interviewed participants. About three quarters of participants indicated being willing to participate in hypothetical medical studies toward the end of life ( n = 30; 81.1%), in HIV biobanking ( n = 30; 81.1%), and in a research autopsy ( n = 28; 75.7%) to advance HIV cure research, mainly for altruistic benefits. The main perceived risks had to do with physical pain and confidentiality. Barriers and facilitators were distributed across five domains: social/professional role and identity, environmental context and resources, social influences, beliefs about consequences, and capabilities. Participants wanted more information about study objectives and procedures, possible accommodations with their last will, and rationale for studies or financial interests funding studies. Our results indicate that older PWHIV would be willing to participate in HIV cure research toward the end of life, HIV biobanking, and research autopsy. However, a dialogue should be initiated to inform participants thoroughly about HIV cure studies, address concerns, and accommodate their needs and preferences. Additional work is required, likely through increased community engagement, to address educational needs.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".