Financing of medical services: experience of foreign countries and Ukraine
Why this work is in the frame
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Bibliographic record
Abstract
Introduction. Research into the sources of health care funding is necessary to develop an effective policy to improve the domestic health care system and improve the accessibility and quality of medical care. The purpose of the article is to assess the sources of funding of medical services in foreign countries and in Ukraine in order to identify and analise current trends and prospects for financing the domestic health care system in the implementation of health care reform. Results. An analysis of trends in the financing of health services in foreign countries has shown that there is a certain imbalance between the country's economic growth and its health care expenditures. The share of health services expenditures in GDP averaged 8.8 % or almost $ 4,000 per OECD citizen in 2018 y . This cost figure is 24 times higher than the per capita health care costs in Ukraine and can be a guide to the amount of funding for medicine in the world community. Citizens of OECD countries, unlike Ukrainians, pay an average of 21 % of all health care costs. The priority sources of funding for one group of countries are budget funds (Norway, Denmark, Sweden, Great Britain, Canada), and for another – compulsory health insurance (Germany, Japan, France, etc.). Сonclusion. Funds of the population are the main source of funding for medical services in Ukraine – 53 %. This indicator is critical for the country, as low-income citizens are unable to pay for medical care and the number of chronic diseases, disability and mortality are increased. The transformational reform of the health care system in Ukraine was started in 2015 and according to international experts is successful and meets international practices of accessibility, quality and efficiency of medical services. Further consistent implementation of health care reform can provide financial protection for the population from excessive out-of-pocket spending, improve access to health care, and improve public health.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it