#109. Registo clínico eletrónico numa clínica dentária universitária – perceção dos estudantes

Objetivos: Avaliação do nível de satisfação e da eficácia do registo clínico eletrónico na Clínica Dentária Universitária da Universidade Católica Portuguesa. Materiais e métodos: Realizou‐se um estudo do tipo observacional transversal, com recurso à aplicação de um questionário (adaptado de Mostafa, 2015) relacionado com o registo clínico eletrónico do programa de gestão clínica Newsoft DS9®, aos estudantes do 4.° e 5.° ano do mestrado integrado em Medicina Dentária. Para análise estatística recorreu‐se ao programa SPSS© V23.0, utilizando estatística descritiva e análise bivariada com o recurso ao teste qui‐quadrado/exato de Fisher. Resultados: Os estudantes consideram que o programa informático é melhor que o registo em papel, mais fácil de aceder, permite a comunicação entre as várias áreas disciplinares e aumenta a produtividade sem uma maior carga de trabalho. No entanto, os participantes referem algumas falhas: velocidade reduzida do processamento e bloqueios informáticos na introdução de dados. Conclusões: Em ambiente universitário, a utilização de um registo clínico eletrónico traduz‐se num grau de satisfação elevado que foi demonstrado pelos estudantes, com várias referências positivas à sua aplicação, porém as limitações informáticas referidas podem condicionar a sua utilização, caso não haja um suporte adequado.info: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 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