OECD AND UKRAINE: TRENDS IN HEALTH CARE FINANCING
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,005 | 0,009 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».