Practical and Ethical Concerns in Implementing Enhanced Surveillance Methods to Improve Continuity of HIV Care: Qualitative Expert Stakeholder Study
Bibliographic record
Abstract
BACKGROUND: Retention in HIV care is critical to maintaining viral suppression and preventing further transmission, yet less than 50% of people living with HIV in the United States are engaged in care. All US states have a funding mandate to implement Data-to-Care (D2C) programs, which use surveillance data (eg, laboratory, Medicaid billing) to identify out-of-care HIV-positive persons and relink them to treatment. OBJECTIVE: The purpose of this qualitative study was to identify and describe practical and ethical considerations that arise in planning for and implementing D2C. METHODS: Via purposive sampling, we recruited 43 expert stakeholders-including ethicists, privacy experts, researchers, public health personnel, HIV medical providers, legal experts, and community advocates-to participate in audio-recorded semistructured interviews to share their perspectives on D2C. Interview transcripts were analyzed across a priori and inductively derived thematic categories. RESULTS: Stakeholders reported practical and ethical concerns in seven key domains: permission and consent, government assistance versus overreach, privacy and confidentiality, stigma, HIV exceptionalism, criminalization, and data integrity and sharing. CONCLUSIONS: Participants expressed a great deal of support for D2C, yet also stressed the role of public trust and transparency in addressing the practical and ethical concerns they identified.
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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 | 0.077 | 0.075 |
| 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 | 0.011 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| 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".