Challenges of NGO-to-state Referral in the Delivery of HIV Prevention Programs in Ukraine Supported by the Global Fund
Bibliographic record
Abstract
BACKGROUND: Ukraine has one of the world's fastest growing HIV rates and was one of the largest recipients of funding from the Global Fund to Fight AIDS, Tuberculosis and Malaria (GF). The objective of this study was to close the gaps in the literature on the delivery of HIV prevention services by NGOs and the perceptions of NGO delivered services, using as an example HIV prevention programs in Ukraine funded by the GF. METHODS: The aim of this qualitative study was to determine how NGO-based services were implemented in the context of a state-owned healthcare system of Ukraine. An ethnographic study, which included 50 participant interviews, was conducted in three oblasts in Ukraine and in the capital, Kyiv, between 2011 and 2013. This article presents some of the findings that emerged from the analysis. RESULTS: Participants reported that NGOs were focused more on reporting numbers of rapid tests, and less on motivating clients to continue onto treatment. The role division between NGOs and the state in HIV services was largely perceived by participants as unclear and challenging. Overall, lack of clarity on the role of government healthcare providers and NGOs in providing HIV services compromised the process of finding, referring, and retaining HIV patients in care. CONCLUSIONS: Gaps in linking HIV patients to the HIV care continuum have been identified as a potentially problematic issue in delivery of HIV prevention services by GF funded NGOs. With an anticipated GF exit from Ukraine, the lack of clearly defined NGO-to-state referrals of HIV patients complicates the transition of NGO run services into state funding. Further steps to improve referral systems are necessary to ensure a smooth transition and enable Ukraine to fight its HIV epidemic effectively.
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) | 0.001 | 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) | 0.000 | 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".