EVIDÊNCIAS DE CONSTRUCTO Y INVARIANCIA FACTORIAL DE LA ESCALA DE ENCAJAMIENTO EN EL TRABAJO EN DISTINTAS ORGANIZACIONES EN EL BRASIL

Na psicologia das organizações e do trabalho, o construto do engajamento no trabalho é compreendido como um estado psicológico ativo que relaciona as emoções como determinantes da satisfação e do prazer do trabalhador na dinâmica organizacional, conduzindo ao envolvimento e dedicação deste. No Brasil, poucos estudos vêm se preocupando com a verificação da qualidade e manutenção de uma medida de engajamento no trabalho em termos temporais. O presente estudo tem como objetivo avaliar a consistência e invariância da estrutura fatorial da escala de engajamento no trabalho. 569 trabalhadores distribuído em duas amostras distintas, com idades acima de 18 anos, 62 % do sexo feminino, 63% de empresa pública e renda econômica acima de 2.000,00 reais, responderam, através de um link enviado para o e-mail pessoal ou rede social, a escala de engajamento no trabalho e dados sociodemográficos. Observaram-se que, na análise exploratória e confirmatória, os indicadores psicométricos garantiram a consistência e invariância da estrutura bi-fatorial da medida de engajamento para o contexto organizacional. Essa escala não apenas corrobora a capacidade de avaliação do construto, mas também a perspectiva teórica abordada com trabalhadores, contribuindo para a auto-avaliação do papel profissional e a função organizacional do trabalhador na gestão de pessoas.In the psychology of organizations and work, the work engagement construct is been understood as an active psychological state that relates emotions as determinants of worker satisfaction and pleasure in organizational dynamics, leading to the involvement and dedication of the worker. In  Brazil, few studies have been worrying about the verification of quality and maintenance of a measure of work engagement in temporal terms. The present study evaluates the consistency and invariance of the factorial structure of the work engagement scale. 569 workers distributed in two distinct samples, aged over 18 years, 62% female, 63% from public companies and economic income above 2,000.00 reais, answered through a link sent to the personal e-mail or their social network the scale of work engagement and sociodemographic data. It was observed that, in the exploratory and confirmatory analysis, the psychometric indicators guaranteed the consistency and invariance of the bi-factorial structure of the measure of engagement for the organizational context. This scale not only corroborates the assessment capacity of the construct, but also the theoretical perspective approached with workers, contributing to the self-evaluation of the professional role and the organizational function of the worker in the management of people.En la psicología de las organizaciones y del trabajo, el constructo del encajamiento en el trabajo es comprendido como un período psicológico activo que relaciona las emociones como distintivas de la satisfacción y del placer del trabajador en la dinámica organizacional, conduciendo al envolvimiento y dedicación de este. En el Brasil, pocos estudios hacen preocupación con la verificación de la cualidad y manutención de una medida de encajamiento en el trabajo en términos temporales; la presente investigación tiene como objetivo evaluar la consistencia e invariancia de la estructura factorial de la escala de encajamiento en el trabajo. 569 trabajadores, distribuidos en dos amuestras distintas, con edades a cima de 18 años, 62% del género femenino, 63% de empresa pública y renta económica a cima de R$ 2.000,00 reales, respondieron a través de un link enviado para el correo electrónico personal o red social, la escala de encajamiento en el trabajo y datos sociodemográficos. Observase que, en el análisis exploratoria y confirmatoria, los indicadores psicométricos garantieran la consistencia e invariancia de la estructura bi-factorial de la medida de encajamiento para el contexto organizacional. Esta escala no solo corrobora a la capacidad de evaluación del constructo, pero también, la perspectiva teórica abordada con trabajadores, contribuyendo para la autoevaluación sobre el papel profesional y la función organizacional del trabajador en la gestión de personas

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