INTERNATIONAL EXPERIENCE IN LEGAL REGULATION OF HEALTH INSURANCE: A COMPARATIVE ANALYSIS
Bibliographic record
Abstract
Objective: This study aims to conduct a comprehensive comparative analysis of international health insurance systems, focusing on the legal regulation of health insurance to identify strengths and weaknesses. The goal is to develop evidence-based recommendations to improve Russia’s mandatory health insurance (MHI) system, addressing issues like funding, access to services, care quality, and legal frameworks. Method: The research applies a theoretical framework built on domestic and international scholarly works related to health insurance and insurance law. The study uses general scientific methods like analysis, synthesis, and comparison, complemented by comparative legal analysis and institutional review. Empirical data are drawn from legal acts, statistical data from organizations like WHO and ILO, and case studies. Results: The research identifies key health insurance models: state-funded (e.g., UK, Canada), social insurance (e.g., Germany, France), and private insurance (e.g., U.S.), each with distinct advantages and challenges. Global trends include an increasing role of the state in health insurance regulation and efforts to expand universal coverage. Countries are evolving from selective to universal health insurance systems, focusing on accessibility, affordability, and quality of care. Novelty: The study highlights the applicability of international models for enhancing Russia’s MHI system, focusing on expanding state regulation and ensuring universal coverage. Key recommendations include improving regulatory frameworks, addressing coverage gaps for specific groups like the self-employed and rural populations, and strengthening accountability among insurers and providers.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".