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

Diretrizes clínicas e outras práticas voltadas para a melhoria da qualidade assistencial em operadoras de planos de saúde sob a perspectiva dos seus dirigentes, no Brasil Clinical guidelines and other practices for improving quality of care by health plans from the perspective of their operators in Brazil

Este estudo objetivou caracterizar a implementação de diretrizes clínicas e outros instrumentos e práticas de melhoria da qualidade nas operadoras de planos de saúde no Brasil. O estudo foi transversal e descritivo, de abrangência nacional, considerando 1.573 operadoras de planos de saúde, que constavam do cadastro da Agência Nacional de Saúde Suplementar. A amostra foi do tipo complexo, estratificada por macrorregião, segmento de mercado e número de beneficiários. Foram entrevistadas 90 operadoras que aceitaram participar. Para a obtenção das estimativas sobre o universo de operadoras de planos de saúde, levou-se em conta um fator de expansão da amostra atribuído por estrato. Apenas 32,3% das operadoras de planos de saúde conduziam o uso de diretrizes clínicas, havendo variação regional e entre segmentos de mercado. A prática de gestão da clínica ainda é muito incipiente. Desafios colocam-se para a incorporação da gestão da clínica como dimensão da gestão nas organizações de saúde, entre as quais, as operadoras de planos de saúde. Iniciativas voltadas para a melhoria da qualidade assistencial precisam ser integradas e conduzidas no nível organizacional.<br>This study aimed to characterize the implementation of clinical guidelines and other instruments and practices for health care quality improvement among health plan operators in Brazil. It was a national cross-sectional descriptive study, initially considering 1,573 health plan operators registered in the National Agency for Supplementary Health Care. The sample design was complex, stratified by macro-region, market segment, and number of beneficiaries. Ninety health plan operators agreed to participate and were interviewed. To obtain estimates for the universe of health plan operators, a sample expansion factor attributed per stratum was considered. Only 32.3% of the health plan operators implemented clinical guidelines, with important variation across regions and market segments. Clinical governance practices are still in the very initial stages. Challenges are presented with regard to health care incorporation as a dimension of management within health care organizations, including health plan operators. Initiatives to improve quality of care need to be integrated and conducted at the organizational level