Ethical Issues in HIV-related Social Network Research Involving Substance-Using Sexual Minorities at Risk for HIV
Bibliographic record
Abstract
Background: Some social network research (SNR) relies on individuals reporting information about network members, with network members not providing consent. We assess how substance-using sexual minorities at risk for HIV perceive the benefits and risks of SNR and the preferred processes for obtaining informed consent. Methods: We conducted 20 qualitative interviews with adults who identified as people of color, were cisgender male and had sex with cisgender men, and reported using substances (<12 months) in San Diego, CA, USA. Participants were asked about perceived risks and benefits of SNR related to HIV, with differing levels of network information being collected. Participants compared the risks of SNR to risks in daily life and were asked about their preferred consent format. Interviews were recorded via zoom, transcribed, and analyzed using qualitative thematic analysis. Results: Participants were Latinx (84%), Black (10%), and 1 Filipino (5%), the median age was 31 years, and 25% of them reported previous research experience. Most viewed SNR favorably and less risky than daily life. Participants preferred study designs where network members are also recruited, as their consent was viewed as "community consent." Participants also felt that community benefits of HIV-related SNR research outweigh the risks. Opinions were mixed about providing identifying information in the context of reporting substance use. A combination of a video using "lay-language" visuals and a written consent format was preferred. Conclusion: Perceived benefits of SNR to HIV prevention and care outweighed the risks, with concerns about providing last names. Researchers should assess whether the collection of last names is warranted.
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 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 | 0.455 | 0.405 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.017 | 0.038 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".