OECD AND UKRAINE: TRENDS IN HEALTH CARE FINANCING
Bibliographic record
Abstract
The article examines current trends in healthcare financing in OECD countries and Ukraine. The focus is on assessing the dynamics of total healthcare spending and it is found that in 2020, the COVID-19 pandemic caused a significant increase in funding in all countries. In particular, only in 2019-2020, the share of healthcare costs in the GDP of OECD countries increased by 1% on average. Thus, advanced countries, realizing the impact of the healthcare industry on the economy and well-being of the country as a whole, tried to maximally strengthen its financial stability in general and to epidemic challenges in particular. The priority sources of health care financing were analyzed and it was found that the governments of the OECD countries diversify the sources of financing in the sector to protect their citizens from excessive financial burden and to ensure affordable and high-quality medical care. It was found that the direct costs of patients from OECR countries account for an average of 20% of all health care costs, while in Ukraine the population finances more than 46% of medical costs. It was noted that this indicator is threatening for the country, since the poor do not have access to medical care due to lack of funds and, as a result, the number of diseases, the level of disability, and mortality of citizens is increasing. It was established that the priority sources of financing for one group of OECD countries (Denmark, Sweden, Norway, Great Britain, Canada, etc.) are budget funds, and for another (Germany, Japan, France, etc.) - funds from the mandatory health insurance system. In recent years, there has been a tendency to increase the share of mandatory health insurance in the structure of financing sources of OECD countries, which increased by 2% on average and amounted to 39%. It was concluded that the Ukrainian health care system, in which the reform began in 2015, annually increases the amount of funding and has positive feedback from WHO and World Bank experts about the results of the reform. However, due to political changes in 2014 and economic constraints due to the COVID-19 pandemic, total health spending in dollar terms in 2020 did not reach the 2013 funding level. In addition, the war made adjustments to the activity of the industry, introducing a regime of maximum preservation of infrastructure, simplification of financing, and ensuring the availability of medical services. Despite this, the government developed a post-war healthcare recovery plan to revive destroyed facilities and radically transform the industry in peacetime.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".