O financiamento dos sistemas universais de saúde e seus impactos na atenção ao usuário idoso. Uma análise à luz da saúde pública / The financing of universal health systems and their impacts on care for the elderly. An analysis in the light of public health

A presente pesquisa trata-se de um estudo no campo da saúde pública, onde abordará questionamentos acerca de acesso, universalidade e economia entre os sistemas universais de saúde. A temática em questão vem sendo pauta de debate e grandes órgãos mundiais, como por exemplo, a Organização Mundial de Saúde (OMS). O estudo possui como questão norteadora: De que forma os países que possuem sistemas universais de saúde lidam com o financiamento da saúde pública dando ênfase à população idosa? Seu objetivo geral é analisar de que forma os países que possuem sistemas universais de saúde lidam com o financiamento da atenção à saúde da população com ênfase à população idosa. O objetivo específico é descrever de que forma os países que possuem sistemas universais de saúde lidam com o financiamento da atenção à saúde pública com enfoque na população idosa. Método: foi realizada uma revisão integrativa. Logo, foram percorridas as seguintes etapas para realização de tal método: 1ª Fase que consiste na elaboração da questão que norteará o estudo. 2ª Fase que realizam busca de amostragem com base nas literaturas. 3ª Fase será a coleta de dados. 4ª Fase consistiu na análise crítica dos estudos selecionados. 5ª Fase foi a discussão dos resultados e a última e 6ª Fase consistirá na apresentação da revisão integrativa. Conclusão: Pôde-se notar, com o resultado trazido pelas discussões da presente pesquisa, que a Organização Mundial de Saúde (ONU) segundo suas prerrogativas legais expõe sobre os direitos de saúde da pessoa idosa e em caso de descumprimento suas penalidades legais. No entanto, apesar das propostas concretizadas a respeito das prioridades assistenciais estreitadas no momento da proposta de sistematização do cuidado universal dos sistemas mundiais de saúde, a saúde da população idosa permanecia oculta nos debates apesar de seus direitos prioritários, como a saúde da gestante e parturiente que teve foco diante aos debates

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost