ATIVIDADE DE ENSINO INTEGRADORA DOS CURSOS DA SAÚDE NA UNIVERSIDADE FEDERAL DO RIO GRANDE DO SUL: SIGNIFICANDO A EXPERIÊNCIA

A Universidade Federal do Rio Grande do Sul (UFRGS) iniciou, em 2012, a atividade de ensino (disciplina) integradora compartilhada entre os cursos de graduação da área da saúde. A disciplina acontece em cenários de prática da Atenção Primária à Saúde e tem como dispositivo pedagógico o estudo do território. Esta pesquisa analisou a experiência da atividade de ensino integradora da UFRGS, buscando a compreensão de seus sentidos para a educação de profissionais da saúde. Foi realizado um estudo exploratório, de abordagem qualitativa, cujo campo de investigação é a atividade de ensino integradora. A produção de dados envolveu a análise do plano de ensino da disciplina e dos portfólios construídos pelos estudantes de um dos grupos de tutoria, de 2012 a 2016. A análise do material textual utilizou a técnica da análise de conteúdo temática na perspectiva de Bardin. Três categorias emergiram do material textual analisado: as vivências no território; o aprendizado junto às Agentes Comunitárias de Saúde; o aprender juntos a trabalhar juntos e os ganhos para a formação a partir de uma disciplina integradora. Competências colaborativas voltadas especialmente à clareza dos papeis e responsabilidades entre diferentes profissões e respeito às responsabilidades e competências de cada profissão marcaram as narrativas dos estudantes. O desafio da proposta está na possibilidade do avanço nas atividades interprofissionais em saúde realizadas por esse grupo, de modo que ultrapassem a descrição da observação do território. Atividades de ensino curriculares na graduação que incluam os princípios da educação interprofissional devem ser estimuladas na formação dos profissionais da 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

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

Projetos e práticas de formação de professores: relatos

Fundação de Amparo à Pesquisa do estado de São Paulo (FAPESP