Avaliação de uma Tecnologia Educacional para a prevenção de violência sexual de jovens mulheres com Deficiência Intelectual

RESUMO: Introdução: Jovens mulheres com Deficiência Intelectual (DI) têm uma maior probabilidade de sofrerem abuso sexual do que garotas sem deficiência, por serem mais vulneráveis. Muitas são infantilizadas e acabam não recebendo orientações sobre sexualidade. Essa falta de informação as deixa mais expostas a situações de risco, fazendo com que o abusador subestime a vítima, acreditando que ela será descredibilizada por conta da deficiência. Objetivo: Avaliar uma Tecnologia Educacional (TE) com o foco na prevenção do abuso sexual, sob a perspectiva de adolescentes e jovens do sexo feminino com Deficiência Intelectual. Metodologia: Trata-se de um estudo de validação, com abordagem qualitativa, realizado junto a 14 jovens com DI, com informações coletadas a partir de observação participante e não participante. O material educativo é formado por um livro, dois bonecos sexuados, um livreto e vídeo explicativo. Resultados: Após a avaliação e percepção das participantes quanto aos quesitos: objetivo, relevância, eficácia, apresentação, acesso, clareza e interatividade, o material foi considerado validado, tendo em vista que a tecnologia contempla pontos importantes a serem discutidos com jovens com DI e cumpre seu propósito de criação. Conclusão: Observa-se como potencial deste estudo o destaque inovador quanto à percepção do público-alvo sobre o material educativo, considerando a capacidade de autonomia e superação das dificuldades advindas com a Deficiência Intelectual. Ressalta-se a importância de continuar o uso da TE, junto a um número maior de mulheres com DI, para que mais percepções sejam colhidas e se reforce sua efetividade

USABILIDADE DE PRODUTOS DE TECNOLOGIA ASSISTIVA PARA ATIVIDADES DE VIDA DIÁRIA DE PESSOAS COM DOENÇA DE PARKINSON

The objective of this field research was to evaluate the usability of 3D Assistive Technology products for the daily activities of patients with Parkinson's disease, considering the variables related to the user and the performance of the task (efficiency, effectiveness and satisfaction). The results allowed to establish requirements for product design, to promote autonomy and independence for the population studied.O objetivo desta pesquisa de campo foi avaliar a usabilidade de produtos de Tecnologia Assistiva, impressos em 3D, para atividades de vida diária de pacientes com Doença de Parkinson, considerando as variáveis ligadas ao usuário e ao desempenho da tarefa (eficiência, eficácia e satisfação). Os resultados permitiram estabelecer requisitos para o projeto de produtos, para favorecer autonomia e independência à população estudada

Repercussões do treinamento com realidade virtual não imersiva nas habilidades motoras manuais de pessoas com doença de Parkinson

Dentre os problemas motores e não motores que acometem as pessoas com Doença de Parkinson, existem as dificuldades na escrita manual, denominada micrografia. No treino com realidade virtual não imersiva para escrita, o terapeuta ocupacional tem competência para planejar o tratamento por meio da análise da atividade, a fim de amenizar e prevenir os impactos da Doença de Parkinson nas ocupações. Objetivo: Analisar as repercussões do treinamento com realidade virtual não imersiva nas habilidades motoras manuais de pessoas com Doença de Parkinson. Métodos: Trata-se de um estudo de intervenção, do tipo ensaio clínico não controlado, de natureza quantitativa. O treinamento com realidade virtual não imersiva contemplou o uso do tablet e dos aplicativos Dexteria®, Smash Hit e Dots. O estudo foi vinculado ao Programa de Extensão Pró-Parkinson e ao Projeto Pró-Parkinson: Terapia Ocupacional. Resultados: A amostra foi composta por dez participantes de ambos os sexos. Em relação à dominância do membro superior, apenas um participante era canhoto. A análise da atividade foi fundamental para estimular as habilidades requeridas pelos aplicativos. De acordo com o analisado, os resultados encontrados são favoráveis aos participantes da pesquisa por corresponder a uma doença neurodegenerativa. A realidade virtual não imersiva é um recurso favorável para fornecer estímulos visuais com propósito de melhorar a velocidade de movimento, refletindo na mobilidade funcional. Conclusão: O treino com os aplicativos selecionados pela técnica de análise da atividade, em realidade virtual não imersiva melhorou e/ou manteve as habilidades motoras dos participantes, mesmo nos estágios mais avançados da doença.Among the motor and non-motor problems that affect people with Parkinson’s disease, there are difficulties in manual writing, called micrography. In training with non-immersive virtual reality for writing, the occupational therapist has the competence to plan treatment through activity analysis, in order to mitigate and prevent the impacts of Parkinson’s Disease on occupations. Objective: To analyze the repercussions of training with non-immersive virtual reality in the motor skills of people with Parkinson’s disease. Methods: This is an intervention study of the non-controlled clinical trial type of a quantitative nature. Training with non-immersive virtual reality included the use of tablet and applications Dexteria®, Smash Hit and Dots. The study was linked to the Pro-Parkinson Extension Program and the Pró-Parkinson Project: Occupational Therapy. Results: The sample consisted of ten participants of both sexes. Regarding the dominance of the upper limb, only one participant was left-handed. Activity analysis was instrumental in stimulating the skills required by the applications. According to the analyzed, the results are favorable to the participants of the research for corresponding to a neurodegenerative disease. Non-immersive virtual reality is a favorable resource to provide visual stimuli for the purpose of improving speed of movement, reflecting functional mobility. Conclusion: Training with applications selected by activity analysis technique, in non-immersive virtual reality improved and/or maintained the motor skills of participants, even in the most advanced stages of the disease

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