MAPEAMENTO DOS PROCESSOS DA COORDENADORIA DE APOIO ADMINISTRATIVO DO CENTRO DE CIÊNCIAS DA SAÚDE DE UMA UNIVERSIDADE PÚBLICA

O mapeamento de processos de serviços possibilita analisar a qualidade das atividades desenvolvidas, o desempenho dos funcionários e da gestão de uma unidade organizativa visando o melhor atendimento a comunidade. Objetivo: mapear os processos da Coordenadoria de Apoio Administrativo (CAA) do Centro de Ciências da Saúde (CCS) de uma Universidade Pública analisando seu desempenho. Método: estudo de caso de abordagem qualitativa com análise documental e observação participante, realizado em jun.2015, na CAA do CCS de uma Universidade Pública, localizada em Florianópolis, SC, Brasil. Resultados: os cinco processos mapeados são de grande importância para a Coordenadoria e dispendem um tempo excessivo na execução de suas atividades. E, apresentam-se propostas de mudanças nos mesmos. Conclusão: é possível minimizar o fluxo destes processos, evitar desperdício de tempo, com trâmites ágeis, corretos, sem engessamento, agilidade nas tomadas de decisões, suprindo a falta de funcionários desta Coordenadoria e melhorar seu atendimento e satisfação da comunidade usuária destes serviços

GESTÃO DE PROCESSOS EM COORDENADORIAS DE APOIO ADMINISTRATIVO: ESTUDO DE CASO NOS CENTROS DE ENSINO DE UMA UNIVERSIDADE PÚBLICA

Este trabalho tem por objetivo avaliar a gestão de processos das Coordenadorias de Apoio Administrativo em universidades, através de um estudo de caso realizado na Universidade Federal de Santa Catarina. Assim, descreve as principais rotinas, identificando como são distribuídas essas atividades, os pontos críticos na execução da gestão de processos e por fim propondo melhorias na gestão de processos. Foi elaborado um instrumento de avaliação, desenvolvido para a realidade das coordenadorias que foram estudadas. Trata-se de uma pesquisa de abordagem qualitativa, que utiliza o estudo de caso como estratégia, com natureza aplicada, utlizando-se de entrevistas semiestruturadas realizadas com gestores das Coordenadorias de Apoio Administrativosos da instituição pesquisada. Também utilizou-se de pesquisa documental e observação participante na coleta dos dados. Os resultados apresentam as descrições dos processos, identificação de processos críticos e necessidades de melhoria

Facial paralysis associated with acute otitis media

Acute otitis media with facial paralysis is not a very frequent association. AIM: the goal of the present investigation was to asses the evolution of facial paralysis caused by acute otitis media. STUDY FORMAT: clinical-retrospective. MATERIALS AND METHODS: we studied 40 patients with this association, from a total of 2758 cases of facial paralysis seen during this time in the department of facial nerve disorders. All the patients were clinically assessed and had epidemiological data, prognostics and evolution. RESULTS AND CONCLUSION: the paralysis was of sudden onset in 95% of the cases. Recovery was of 85% for grade I (House-Brackman) and 15% for grade II (House-Brackman). Treatment was clinical, with antibiotics and steroids - yielding good results. In those patients with electrical bad prognosis, facial nerve decompression turned their evolution into a favorable one.A otite média aguda com paralisia facial não é uma associação muito freqüente. OBJETIVO: O objetivo deste trabalho foi avaliar a evolução da paralisia facial decorrente de otite média aguda. FORMA DE ESTUDO: Clínico retrospectivo. MATERIAL E MÉTODO: Foram estudados 40 pacientes com esta associação de patologias, num total de 2758 casos de paralisa facial atendidos neste período no setor de distúrbios do nervo facial. Todos os pacientes foram avaliados clinicamente com dados epidemiológicos, prognósticos e evolutivos. RESULTADOS E CONCLUSÃO: A paralisia foi súbita em 95% dos casos. A recuperação foi de 85% para o grau I (House-Brackman) e 15% para o grau II (House-Brackman). O tratamento foi clínico com antibiótico e corticoterapia com bons resultados. Nos pacientes com mau prognóstico elétrico a descompressão do nervo facial fez com que a evolução fosse favorável.Universidade Federal de São Paulo (UNIFESP) Escola Paulista de Medicina Departamento de Otorrinolaringologia e Cirurgia de Cabeça e PescoçoUNIFESP, EPM, Depto. de Otorrinolaringologia e Cirurgia de Cabeça e PescoçoSciEL

Taxonomic status and phylogenetic relationships of Marmosa agilis peruana Tate, 1931 (Didelphimorphia: Didelphidae), with comments on the morphological variation of Gracilinanus from central-western Brazil

The marsupials of the family Didelphidae went through profound taxonomic rearrangements in recent decades, mainly related to an increase in the number of specimens deposited in scientific collections and the inclusion of molecular data in systematic analyses, resulting in better resolved phylogenies and taxa delimitation. Analyses of a large series of the gracile mouse opossum Gracilinanus agilis, including types and complementary material, recovered specimens assignable to Marmosa agilis peruana Tate, 1931 as a monophyletic group that is diagnosable by unique morphological, morphometric and molecular datasets, meriting its recognition as a full species. Here we provide an emended diagnosis, description and comparisons with congeners for G. peruanus. The former species differs from the latter by the dull reddish dorsal pelage, smaller general size, position of the maxillary fenestrae, presence of accessory cusps in upper canines, and morphology of the alisphenoid tympanic process. It ranges from central Peru to central Bolivia and western Brazil in the states of Rondônia and northwestern Mato Grosso, where it occurs in sympatry with G. agilis. Many collecting localities lie in areas with high diversity of non-volant small mammals and accelerated deforestation processes, highlighting its importance in terms of biogeographic studies and conservation policies. © 2014 The Linnean Society of London

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