Human-geographical peculiarities of the healthcare system of Ukraine in the conditions of modern challenges
Bibliographic record
Abstract
Relevance. The paper examines the issues of the health care system development of Ukraine in the context of modern challenges. Today, there are many global environmental, socio-demographic, and economic problems threatening the existence of human civilization. One of the problems was the spread of coronavirus infection COVID-19, which demonstrated unpreparedness of Ukraine and post-socialist countries' health care systems. These countries are undergoing health care transformations, but they do not meet modern world norms and standards. The purpose of the article is to establish the key features of the health care system of Ukraine during its transformation given the positive experience of medical systems in the world, from the positions of human geography to identify current challenges and to assess the ability to respond to social demand and the threat of the global crisis in the form of new diseases, the spread of epidemics threatening to human health, quality and life expectancy. Methods. This research is conducted on the basis of human-geographical approach with use of the set of methods and tools to analyze the health care system, which is extremely important for obtaining verified and scientifically sound results. In particular, the authors used methods of induction and deduction, comparison, formalization, analogy, analysis, systematization, including ranking and grouping, historical, graphical, mathematical and statistical, SWOT-analysis methods. Results. Scientific novelty and practical significance. The features, advantages and disadvantages of existing models of health care systems in different countries were identified. In particular, models of medical systems were considered: a model of the single-payer, model of obligatory insurance, and hybrid system. The peculiarities of the formation of the health care system of Ukraine were determined, the key features and principles of the M.O. Semashko’s system were identified, its positive and negative features preserved to this day were outlined. The distribution of European and post-socialist countries was analyzed according to the indicators of state budget expenditures on health care and GDP, number of doctors, hospital beds per capita. The transformational processes in the health care system of Ukraine, the peculiarities of the medical reform in Ukraine were revealed, the peculiarities of the development of the medical system in the conditions of the pandemic were characterized. The SWOT analysis identified the strengths and weaknesses of the Ukraine’s health care system in terms of reform and transformation, its opportunities and threats in the light of current challenges.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".