Biomedical HIV Prevention Including Pre-exposure Prophylaxis and Opiate Agonist Therapy for Women Who Inject Drugs
Bibliographic record
Abstract
Women who inject drugs (WWID) are at higher risk of HIV compared with their male counterparts as a result of multiple factors, including biological, behavioral, and sociostructural factors, yet comparatively little effort has been invested in testing and delivering prevention methods that directly target this group. In this article, we discuss the need for expanded prevention interventions for WWID, focusing on 2 safe, effective, and approved, yet underutilized biomedical prevention methods: opiate agonist therapy (OAT) and oral pre-exposure prophylaxis (PrEP). Although both interventions are well researched, they have not been well examined in the context of gender. We discuss the drivers of women injectors' higher HIV risk, review the effectiveness of OAT and PrEP interventions among women, and explain why these new HIV prevention tools should be prioritized for WWID. There is substantial potential for impact of OAT and PrEP programs for WWID in the context of broader gender-responsive HIV prevention initiatives. Although awaiting efficacy data on other biomedical approaches in the HIV prevention research "pipeline," we propose that the scale-up and implementation of these proven, safe, and effective interventions are needed now.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".