Assessing the influence of conflict on the dynamics of sex work and the HIV and HCV epidemics in Ukraine: protocol for an observational, ethnographic, and mathematical modeling study
Bibliographic record
Abstract
BACKGROUND: Armed conflict erupted in eastern Ukraine in 2014 and still continues. This conflict has resulted in an intensification of poverty, displacement and migration, and has weakened the local health system. Ukraine has some of the highest rates of HIV and Hepatitis C (HCV) in Europe. Whether and how the current conflict, and its consequences, will lead to changes in the HIV and HCV epidemic in Ukraine is unclear. Our study aims to characterize how the armed conflict in eastern Ukraine and its consequences influence the pattern, practice, and experience of sex work and how this affects HIV and HCV rates among female sex workers (FSWs) and their clients. METHODS: We are implementing a 5-year mixed methods study in Dnipro, eastern Ukraine. Serial mapping and size estimation of FSWs and clients will be conducted followed by bio-behavioral cross-sectional surveys among FSWs and their clients. The qualitative component of the study will include in-depth interviews with FSWs and other key stakeholders and participant diaries will be implemented with FSWs. We will also conduct an archival review over the course of the project. Finally, we will use these data to develop and structure a mathematical model with which to estimate the potential influence of changes due to conflict on the trajectory of HIV and HCV epidemics among FSW and clients. DISCUSSION: The limited data that exists on the effect of conflict on disease transmission provides mixed results. Our study will provide rigorous, timely and context-specific data on HIV and HCV transmission in the setting of conflict. This information can be used to inform the design and delivery of HIV and HCV prevention and care services.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".