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Enregistrement W2755656128

COMPARATIVE STUDY OF FAIR FINANCING IN THE HEALTH INSURANCE

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

Notice bibliographique

RevueJournal of Healthcare Management · 2017
Typearticle
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueHealthcare Systems and Reforms
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésActuarial sciencePaymentBusinessHealth insuranceGeneral insuranceHealth careSocial determinants of healthFinanceInsurance policyEconomicsEconomic growth
DOInon disponible

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,093
Score d'incertitude au seuil0,992

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0040,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,109
Tête enseignante GPT0,350
Écart entre enseignants0,241 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations3
Publié2017
Routes d'admission1
Résumé présentoui

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