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

Consulta de enfermagem ginecológica: eficácia do tratamento de rotina nas vulvovaginites

Este estudo tem como objetivo propor as Instituições de Saúde que utilizam o Enfermeiro na realização de consulta ginecológica a adotarem rotina de medicamentos para o tratamento das vulvovaginítes. Para isso, obteve-se uma relação dos agentes etiológicos mais freqüentes encontrados no exame colpocitológico, tratamento empregado e uma análise da eficácia deste tratamento de rotina. Considerou-se ainda a freqüência das clientes à consulta de Enfermagem Ginecológica

Consulta de enfermagem ginecológica: eficácia do tratamento de rotina nas vulvovaginites

Este estudo tem como objetivo propor as Instituições de Saúde que utilizam o Enfermeiro na realização de consulta ginecológica a adotarem rotina de medicamentos para o tratamento das vulvovaginítes. Para isso, obteve-se uma relação dos agentes etiológicos mais freqüentes encontrados no exame colpocitológico, tratamento empregado e uma análise da eficácia deste tratamento de rotina. Considerou-se ainda a freqüência das clientes à consulta de Enfermagem Ginecológica

SOCIAL SUPPORT NETWORKS FOR WOMEN IN SITUATIONS OF VIOLENCE BY AN INTIMATE PARTNER

Objetivo: analizar la red social y los tipos de apoyo a las mujeres en situación de violencia por el compañero íntimo. Métodos: investigación cualitativa y analítica, anclada en la Teoría de Red Social de Sanicola. Los actores sociales fueron 20 mujeres que vivenciaron violencia por el compañero entrevistadas en un Centro Especializado de Atención en Rio de Janeiro, Brasil. Se utilizó un instrumento semi-estructurado e individual. Para elaborar el mapa de la red social se le solicitó a las mujeres que auxiliaran al investigador en la confección del diseño representativo de sus relaciones con personas e instituciones en el contexto de la violencia. El abordaje teórico permitió el entendimiento de las dinámicas relacionales sociales en que la mujer estuvo inmersa para el enfrentamiento de la violencia. Los discursos fueron organizados por el método de análisis de contenido. Resultados: la red social primaria de las mujeres fue compuesto por hijos, hermanos, yernos, nueras y amigos que prestaron apoyo emocional y material. La red secundaria fue representada por los servicios por los servicios y apoyo informativo obtenidos en el hospital, puesto de salud, centro especializado de atendimiento y delegaciones. Las mujeres tuvieron mayor proximidad y vinculo fuerte con los hijos, nueras y yernos; mantuvieron distancia de los padres y tuvieron vínculos conflictivos en la relación con el compañero íntimo. Conclusión: vínculos descontinuos o frágiles, en la mayoría, fueron identificados con elementos de la red primaria; y los normales o fuertes, con la red secundaria. Las mujeres están distantes de los miembros de la red primaria y próximas de la red secundaria, estableciendo vínculos por la recepción y atención resolutiva. El mapa de la red social construido con las mujeres fue determinante para la intervención de apoyo para autoestima