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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".