A utilização do diagnóstico situacional para o planejamento das ações na ESF/ The use of situational diagnosis for action planning in the ESF

O Diagnóstico Situacional é um dispositivo que tem a finalidade de coletar e analisar os dados referentes as condições de saúde e risco de determinada população. Estes dados são importantes e servem como base para a decisão nas ações e serviços da Atenção Básica (AB). Essa pesquisa tem como objetivo analisar a produção cientifica que aborde a utilização do diagnóstico situacional para realização do planejamento das ações na Estratégia de Saúde da Família (ESF). Para o estudo foi adotado o método de revisão integrativa da literatura nas bases de dados: SciELO, LILACS e Biblioteca Virtual de Saúde (BVS), de outubro e novembro de 2020, utilizando quatro combinações de descritores que totalizaram sete produções. Foram selecionados os artigos completos, no idioma português, publicados nos últimos cinco anos, os quais abordassem o tema. Após isso, os dados foram catalogados e analisados a luz da literatura pertinente. Os estudos apontaram a importância do diagnóstico situacional para a compreensão das demandas de cuidados no território de atuação da ESF, servindo para desenvolver melhores estratégias de atenção integral, decisões assertivas no processo de trabalho em equipe, além de propiciar uma maior interligação entre a comunidade e os profissionais de saúde

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