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Record W2755656128

COMPARATIVE STUDY OF FAIR FINANCING IN THE HEALTH INSURANCE

2017· article· en· W2755656128 on OpenAlexaboutno aff
Mohammad Saadati, Ramin Rezapour, Naser Derakhshani, Maryam Naghshi

Bibliographic record

VenueJournal of Healthcare Management · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Systems and Reforms
Canadian institutionsnot available
Fundersnot available
KeywordsActuarial sciencePaymentBusinessHealth insuranceGeneral insuranceHealth careSocial determinants of healthFinanceInsurance policyEconomicsEconomic growth
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.109
GPT teacher head0.350
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2017
Admission routes1
Has abstractyes

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