The CanCURE Survey: Gender-Based Differences in HIV Cure Research Priorities
Bibliographic record
Abstract
Background: The Canadian HIV Cure Enterprise (CanCURE) is a pan-Canadian research collaboratory, investigating approaches for achieving sustainable HIV remission. In preparation for the next research cycle, CanCURE researchers and the Community Advisory Board (CAB) co-designed a web-based survey to identify HIV research priorities from the perspective of people with HIV (PWH) in Canada. The current study examined gender-based differences in these priorities. Methods: From August to December 2024, we recruited PWH across Canada through community organizations and community members. We collected data using REDCap electronic data capture tools hosted at The Research Institute of the McGill University Health Centre. The survey included 36 demographic questions, 16 questions related to general knowledge about HIV and HIV cure-related concepts, and 21 questions ranking research priorities. Knowledge questions were multiple choice, while priorities could be ranked on a scale. We summarized participant characteristics via descriptive statistics, and the research priorities were further stratified according to gender. Results: Of 109 participants, 48.6% self-identified as men, 46.8% as women, and 4.6% as two-spirit, non-binary, agender, or other. The median age was 53 years old. Approximately one-third of participants had lived with HIV for ≤14 years, one-third for 15–24 years, and one-third for ≥25 years. Overall, the median knowledge score of respondents was 79%. Among the 78 participants with prior HIV research experience, three times as many men (61.1%) as women (19.0%) participated in interventional studies involving medication or medical procedures. Men ranked preventing HIV transmission to partners as a priority, studying where the virus hides as the second, and avoiding high comorbidity risks as the third. In contrast, women ranked not having to take pills daily as a priority and avoiding higher risks for comorbidities as the second priority. Both genders equally valued expanding community involvement in HIV cure research. However, men focused more on integrating social and behavioural research, while women emphasized the need for diverse ethnic representation in research. Conclusions: Although both men and women share some common priorities regarding HIV cure research, there are notable gender differences in their specific concerns. Furthermore, a significant gender gap in participation in interventional studies, essential for advancing HIV cure research, highlights the importance of aligning research priorities with concerns of both genders.
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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 | 0.041 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.006 |
| 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; both teacher heads agree on what is shown here.
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".