COMPARATIVE STUDY OF FAIR FINANCING IN THE HEALTH INSURANCE
Bibliographic record
Abstract
Introduction: Fair financing contribution is one of the main objectives the healthcare systems in the world. Insurance system is one of the most common methods of financial protection against the cost of healthy people is considered. This study aimed to evaluate the comparative insurance system in different countries were performed. Methods: This comparative study was conducted in 2017. First, a comprehensive literature search was conducted through relevant and valid databases and websites to extract scientific evidence. After the screening of findings, Data related to the fairness financing, including the out of pocket, catastrophic payment and fair financing contribution was extracted. Garden classification framework used to match the indicators with models of health insurance. Results: In countries studied, four model finance and insurance including: national health insurance (NHI), national medical system (NHS), social health insurance (SHI) and private insurance was used. France and Australia are the countries where the two models are used simultaneously. The lowest rate of pay out of pocket and catastrophic health expenditure for households in France (6 and 0.01 percent), which uses public and private health insurance model. Britain, Denmark, Canada and Germany, respectively, have the highest indices were fair participation in financing. Conclusion: According to the study it can be concluded that social insurance, national insurance and national health systems can have a good performance in financial protection of the population, So can say insurance system establishing a significant role in financial protection against the cost of people's health. Of course is to be mentioned for choose the model insurance countries should be based on infrastructure and resources available in every country so well able to play its role. Introduction: Fair financing contribution is one of the main objectives the healthcare systems in the world. Insurance system is one of the most common methods of financial protection against the cost of healthy people is considered. This study aimed to evaluate the comparative insurance system in different countries were performed.Method: This comparative study was conducted in 2017. First, a comprehensive literature search was conducted through relevant and valid databases and websites to extract scientific evidence. After the screening of findings, Data related to the fairness financing was extracted. Garden classification framework used to match the indicators with models of health insurance.Result:In countries studied, four model finance and insurance including: national health insurance (NHI), national medical system (NHS), social health insurance (SHI) and private insurance was used. The lowest rate of pay out of pocket and catastrophic health expenditure for households in France (6 and 0.01 percent), which uses public and private health insurance model. Britain, Denmark, Canada and Germany, respectively, have the highest indices were fair participation in financing.Conclusion: According to the study it can be concluded that social insurance, national insurance and national health systems can have a good performance in financial protection of the population, So can say insurance system establishing a significant role in financial protection against the cost of people's health. Of course is to be mentioned for choose the model insurance countries should be based on infrastructure and resources available in every country so well able to play its role.
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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 | 0.015 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".