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

Avaliação de resultados e indicadores da Nursing Outcomes Classification (NOC) em pacientes com transtorno de pânico

O transtorno de pânico (TP) é uma doença crônica caracterizada pela presença de ataques súbitos de ansiedade em conjunto com a intensa sensação de medo e desconforto. Dentre as modalidades de tratamento eficazes está a terapia cognitivocomportamental em grupo (TCCG) que no Hospital de Clínicas de Porto Alegre (HCPA) é coordenado por uma enfermeira. A avaliação inicial do paciente é realizada em consulta de enfermagem onde são utilizadas as etapas do Processo de Enfermagem (PE) para a definição do Diagnóstico de Enfermagem (DE), por meio da Taxonomia NANDA Internacional (NANDA-I) e as intervenções de enfermagem são determinadas com a Nursing Interventions Classification (NIC). No entanto, a etapa da avaliação de resultados segundo a Nursing Outcomes Classification (NOC), ainda não foi implementada. Os objetivos do estudo foram definir os DEs e os resultados da NOC com as respectivas definições conceituais e operacionais para os indicadores estabelecidos na avaliação dos pacientes com TP; e avaliar a evolução clínica dos pacientes por meio de resultados da NOC antes e após o término de um grupo piloto de TCCG para TP. Trata-se de um estudo misto, desenvolvido em duas etapas. A primeira etapa compreendeu uma pesquisa metodológica que utilizou o consenso de quatro especialistas em saúde mental e em PE para a escolha dos resultados e indicadores da NOC. Posteriormente ao consenso, foi realizada a revisão da literatura para elaborar as definições operacionais dos indicadores selecionados. A segunda etapa foi uma pesquisa de resultados, com a aplicação do instrumento elaborado para avaliar os pacientes com TP durante o grupo de TCC, no início, no meio e no final das 12 sessões. A pesquisa foi aprovada pelo Comitê de Ética em Pesquisa do HCPA. Os DEs definidos para os pacientes com TP foram: Medo (00148); Ansiedade (00146); Tristeza crônica (00137); Enfrentamento Ineficaz (00069) e Resiliência Prejudicada (00210). Após o consenso, as especialistas selecionaram cinco resultados NOC: Nível de Medo (1210), com quatro indicadores; Nível de Ansiedade (1211), com seis indicadores; Nível de depressão (1208), com cinco indicadores; Enfrentamento (1302), com três indicadores e Autocontrole da Ansiedade (1402) com três indicadores. As definições conceituais e operacionais foram determinadas para cada indicador. Na segunda etapa, sete pacientes concluíram a TCCG. Ocorreu uma mudança significativa em pelo menos um dos indicadores de cada resultado NOC avaliado no final da TCCG. Portanto, o consenso entre especialistas permitiu selecionar os resultados mais apropriados para a avaliação de pacientes com TP. Os resultados do grupo piloto apontaram para a viabilidade da avaliação da evolução dos sintomas dos pacientes com TP por meio dos resultados de enfermagem NOC em cenário ambulatorial