Health financing in Ukraine:Reform, resilience and recovery
Bibliographic record
Abstract
In 2017, with the adoption of the Law on Financial Guarantees of Health-care Services, the Government of Ukraine launched fundamental reforms to the health financing system with the goals of improving access to essential health services for the population and providing enhanced financial protection from excessive out-of-pocket payments.<br/><br/>Since then, a unified benefit package and a single purchaser of health services have been established with gradual expansion of entitlements and increased primary health care enrollment. The e-health system supporting reforms has become more sophisticated. The WHO and the World Bank have been supporting these reforms, including through periodic joint reviews about progress and challenges.<br/><br/>This is the third joint review and it examines progress of the reforms’ implementation, considering the adaptations and impact of the full-scale invasion of Ukraine by the Russian Federation in February 2022 resulting in economic, demographic and epidemiological shocks. Ukraine’s health system and the overall design of the health reforms have demonstrated significant resilience in responding to the challenges brought about by the war.<br/><br/>The report also highlights the importance of strong institutions, accountability and transparency for the success of the reforms and continued trust in the health system during the war-related response and during post-war recovery in Ukraine.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".