Analysis of the development of voluntary health insurance in OECD countries
Bibliographic record
Abstract
The object of the study is the health care systems of the OECD countries. The subject of the study is voluntary health insurance in OECD countries. The purpose of the study is to identify trends and parameters for the development of voluntary health insurance in OECD countries. Within the framework of this scientific study, methods of data analysis and synthesis, their grouping and graphical interpretation were used. In the text of the article, the author points to the current trends and development parameters of voluntary health insurance in the OECD countries. Particular attention is paid to identifying countries with a high level of coverage of voluntary health insurance by the population, as well as assessing the socio-economic indicators of OECD countries in terms of levels of development of voluntary health insurance. The main conclusions of the study are related to the impact of the development of the voluntary health insurance system on the growth of life expectancy as one of the key components of the quality of life. A special scientific novelty is the obtained comparative grouping of countries depending on the level of development of voluntary medical insurance. Countries with a high level of voluntary health insurance coverage include Australia (54%), Canada (68%), Ireland (47%), Slovenia (89.8%). The results obtained in the course of the study are a good methodological basis for improving the healthcare system in the Russian Federation in the context of achieving the national development goals of our state until 2030, determined by the President of Russia in July 2020.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".