Terapias voltadas para o tratamento do transtorno dissociativo de identidade

O transtorno dissociativo de identidade compreende uma condição psicológica complexa provavelmente causada por inúmeros fatores, envolvendo trauma grave na primeira infância, como abuso sexual, físico ou emocional repetitivo e extremo e repetitivo. Este estudo teve como objetivo identificar as terapias voltadas para o tratamento do transtorno dissociativo de identidade. Para isso, foi realizada uma revisão integrativa de literatura, selecionando fontes a partir das bases de dados Medline e Lilacs. A partir da análise qualitativa de dados, concluiu-se que há vários tipos de terapias para o tratamento de pessoas transtorno dissociativo de identidade, devendo essas serem aplicadas conforme cada realidade. Nos estudos, foram identificados os modelos de tratamento psicanalítico relacional, fásico, psicoativo e psicotraumatológico. Em todos esses, foram registrados resultados satisfatórios, tais como a diminuição na dissociação e o aumento do funcionamento adaptativo do paciente, revelando a possibilidade de desconstruir crenças solidamente cultivadas e trazendo esperança aos pacientes no sentido de amenizar ou superar esse transtorno e garantir uma boa interação social

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