HIV Data and Public Health Ethics
Bibliographic record
Abstract
Abstract Uses of clinical data about people living with HIV (PLHIV) in US public health programs have expanded during the 2010–2020s. The digitization of the healthcare system and recognition that PLHIV who are virally suppressed cannot transmit have contributed to policy mandates for health departments to use routinely collected clinical HIV data to identify PLHIV who have fallen out of care—or who may be in transmission networks—and then (re-)link them to care. The ethics of these programs have been a source of controversy among bioethics scholars, social scientists, PLHIV networks, civil society actors, and others. Debates have focused on privacy and confidentiality, criminalization, community and stakeholder engagement, consent, and programs’ evidence base. The fundamental ethical question is: if clinical HIV data are collected for the benefit of individual patients, does the fact that those data can potentially benefit population health mean that they ought to be used for public health action? In our view, programs that utilize routinely collected clinical HIV data for public health purposes have inadequately accounted for ethical dilemmas raised by infrastructural transformations, biomedical advances, and policy shifts. We propose engaging stakeholders in an ethical reset to shape future developments regarding HIV data and public health.
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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 | 0.196 | 0.162 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.104 |
| Scholarly communication | 0.022 | 0.017 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.024 | 0.025 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".