HIV Pre-Exposure Prophylaxis in Central and Eastern Europe-Gains and Challenges in An Ever-Changing World
Bibliographic record
Abstract
Pre-exposure prophylaxis (PrEP) is an effective prevention tool for controlling the HIV epidemic. Since its approval in the United States in 2012 and Europe in 2016, it has become available on a global scale offered as a registered strategy in clinical studies or demonstration projects with a slow and steady increase. In the second quarter of 2022, PrEP became available in 78 countries globally, with around 3 million people having started using PrEP. Europe has been much slower than the rest of the world to roll out PrEP; nevertheless, currently, PrEP is nationally available and reimbursed in 21 countries; generics are available but not reimbursed in 14 countries. PrEP is not formally implemented in 20 countries, which are mostly Central and Eastern European countries. There are significant disparities between countries in terms of PrEP availability, accessibility, and usage, most likely due to social, cultural, and political differences. The major barriers to PrEP use are reported to be lack of knowledge of people in need, not being reimbursed, and low perception of HIV. PrEP uptake globally and regionally still lacks the power to have an impact on controlling the epidemic. High prioritization of PrEP targets will offer us a realistic chance of reaching the Joint United Nations Programme on HIV/AIDS (UNAIDS) goal of a 90% reduction in HIV infections by 2030 compared to 2010.
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 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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".