Changing the PrEP Narrative: A Call to Action to Increase PrEP Uptake among Women
Bibliographic record
Abstract
Although the incidence of new cases of human immunodeficiency virus (HIV) has decreased in the past decade, in 2018 more than 7000 women with HIV were diagnosed in the United States (US). Globally, per recent reports, 48% of the new HIV infections were among women. There is still no vaccine to prevent HIV transmission. However, pre-exposure prophylaxis (PrEP) was approved in 2012 by the Food and Drug Administration, providing a powerful tool to block HIV infection and help prevent the subsequent development of acquired immunodeficiency syndrome (AIDS). The uptake of PrEP has been slow globally and among the most vulnerable populations in the US, even though the Centers for Disease Control (CDC) recommended its use in high-risk populations, including women. Furthermore, women represent one-quarter of people living with HIV in the US; however, PrEP is underutilized in this group. Thus, it is imperative to make women’s voices heard through conducting more research, ensuring sufficient access to PrEP, and enhancing knowledge about PrEP as a viable prevention strategy for women. This article aims to promote women’s health by changing the narrative, providing key information on empowering women, and increasing the usage of PrEP.
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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 | 0.031 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.010 | 0.018 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".