Multidimensional assessment of fatigue in primary care: The Portuguese checklist of individual strength (CIS-20P)

Dissertação de Mestrado apresentada no ISPA - Instituto Universitário para a obtenção de grau de Mestre na especialidade de Psicologia da SaúdePropósito: fadiga, reportada por muitos pacientes, leva ao uso de recursos do sistema de saúde e a falta de bem-estar mental. Este estudo visa validar a Checklist of Individual Strength portuguesa (CIS-20P) para pacientes dos cuidados primários e desenvolver a primeira distribuição percentual da escala. Método: a amostra deste estudo consiste em 956 participantes: 418 participantes de um centro de cuidados primários (CCP; idades entre 18 e 99; M=55.5; DP=18.82); e 538 participantes de uma amostra online (PO; idades entre 18 e 64; M=39.46; DP=8.43). Resultados: análise factorial confirmatória com os adultos da CCP (participantes com menos de 65 anos) foi satisfatória. Com exceção da dimensão motivacional, os índices de fiabilidade foram satisfatórios. Análise de invariância estrutural entre adultos do CCP e PO provou quase total invariância de items, assim como entre adultos e Idosos do CCP. Fadiga e qualidade do sono previram 41.6% da variação do bem-estar mental no adultos do CPP. Conclusão: a CIS-20P é uma ferramenta válida para acessar níveis de fadiga em pacientes adultos dos cuidados primários. Contudo, apesar de válida para idosos dos cuidados primários, o seu uso não é recomendado neste momento. Investigação a essa população e suas limitações específicas devem ser realizadas. Distribuição percentual revelou maiores indices de fadiga quando comparada à população Holandesa. Distribuição percentual criou uma linha de base para futuros estudos da população portuguesa. São feitas recomendações para investigações futuras da tetra-dimensionalidade da CIS-20P.Purpose: fatigue is widely reported by patients, leading to the use of healthcare resources and decreased mental well-being. This study aims to validate the Portuguese Checklist of Individual Strength (CIS-20P) for the primary care patients and develop its first percentile distribution. Method: the pool of this study consists of 956 participants: 418 participants from a primary health care center (HCC; aged between 18 and 99; M=55.5; SD=18.82); and 538 participants from an online sample (OP; aged between 18 and 64; M=39.46; SD=8.43). Results: confirmatory factor analysis with HCC adults (aged less then 65 years old) was satisfactory. With the exception of the motivation sub-scale, internal consistency estimates were satisfactory. Analysis of structural invariance between the HCC Adults and OP samples proved overall invariance between items as well as between HCC adults and HCC elderly samples. Fatigue and poor sleep predicted 41.6% of the variance in mental well-being in the HCC adults. Conclusion: the CIS-20P is a valid tool in assessing fatigue levels in primary care adult patients. Despite also valid with primary care elderly patients, its use is discouraged this time. Further investigation into this population and its particular limitations must be conducted. Percentile distribution created a baseline for future research of fatigue in Portugal. Recommendations for further research into the CIS-20P tetra-dimensional structure are made

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

Characterisation of microbial attack on archaeological bone

As part of an EU funded project to investigate the factors influencing bone preservation in the archaeological record, more than 250 bones from 41 archaeological sites in five countries spanning four climatic regions were studied for diagenetic alteration. Sites were selected to cover a range of environmental conditions and archaeological contexts. Microscopic and physical (mercury intrusion porosimetry) analyses of these bones revealed that the majority (68%) had suffered microbial attack. Furthermore, significant differences were found between animal and human bone in both the state of preservation and the type of microbial attack present. These differences in preservation might result from differences in early taphonomy of the bones. © 2003 Elsevier Science Ltd. All rights reserved