EPISTEMOLOGIA/EPISTOLOGRAFIA: NOTAS PARA UMA CRÍTICA

A presente explanação desenvolve-se a partir das (MIGNOLO, 2003) cartas publicadas do escritor mineiro Fernando Sabino: Cartas na mesa (2002), Cartas a um jovem escritor e suas respostas (2003) e Cartas perto do coração (2011). Lidas em conjunto e de forma linear as cartas são (des)locadas de seu lócus (abstrato) original, isto é, o espaço intimo da convivência com os amigos. Tal fato já indica relevante característica do texto epistolar sinalizado no título da explanação, “pensamento nômade”, trata-se de uma metáfora fornecida por Brigitte Diaz em seu livro O gênero epistolar ou o pensamento nômade (2016) e que evoca, de forma apropriada, a natureza andarilha das cartas. Uma outra metáfora necessária e que transcende o texto é a das “teorias itinerantes” (MIGNOLO, 2003), sua presença justifica-se ao provocar reflexões em torno da teorização epistolográfica, visto que várias teorias já viajaram por cartas, como a psicanalise, fato que Jacques Derrida em Mal de arquivo (2001) menciona, e as considerações modernistas presente nas cartas de intelectuais como Mário de Andrade. Mas, e a teoria da carta? Terá ela viajado através do texto epistolar? Ou então foi forçada a, tal como um intruso, viajar a reboque nos espaços liminares da teorias européias? Ou, então, sua viajem foi inviabilizada justamente por ser “teoria” e não teorização?. Leitura essencialmente metafórica está envolta nas considerações de Walter Mignolo em Histórias locais/projetos globais (2003) acerca da dupla natureza da fronteira, geográfica e epistemológica, a fim de sustentar que a teorização epistolar nasce a partir das fronteiras

Factors associated with body image dissatisfaction in a Brazilian university sample during the COVID-19 pandemic

We investigated the prevalence of body image dissatisfaction (BID) and associated factors among professors and undergraduate students in Brazil during the COVID-19 pandemic. Using Stunkard’s Figure Rating Scale, BID was analyzed in a sample of 2,220 adults. The independent variables were sociodemographic, lifestyle, mental health symptoms, COVID-19-related factors, disordered eating, experience of weight stigma, and weight change concerns. We used a multinomial logistic regression analysis. The overall prevalence of BID was 82.5% (69.0% due to excess weight), with more professors dissatisfied by excess weight than undergraduate students (78.9% vs. 61.2%, p < 0.001). In the adjusted model, being a young adult (PR, 1.201, 95% CI: 1.128; 1.279), married or in a stable union (PR, 1.088, 95% CI: 1.027; 1.152), reporting of binge eating episode (PR, 1.120, 95% CI: 1.068; 1.173), concern about weight gain (PR, 1.394, 95% CI: 1.310; 1,483), and experience of excess weight stigma (PR, 1.193, 95% CI: 1.141; 1.248) increased the prevalence of BID due to excess weight. While males (PR, 1.578, 95% CI: 1.328; 1.875), moderate to severe depressive symptoms (PR, 1.217, 95% CI: 1.011; 1.465), the concern of losing weight (PR, 1.494, 95% CI: 1.221; 1.830), and experience of low weight stigma (PR, 2.620, 95% CI: 2.093; 3.280) increased the prevalence of BID due to low weight. Different factors associated with BID were observed between students and professors. Bearing in mind the complexity of body image, it is essential to consider different public health interventions and the COVID-19 pandemic’s influence on reducing BID among Brazilian adults, especially susceptible groups

Aspectos metodológicos e desafios da Coorte On-line Comportamento Alimentar e Saúde Mental (COCASa) de docentes e discentes universitários durante a pandemia da COVID-19

O distanciamento social adotado para controle da COVID-19 obrigou Instituições de Ensino Superior (IES) a aderirem a novas estratégias para realização das atividades acadêmicas e muitas pesquisas passaram a ser realizadas em ambientes virtuais. O objetivo deste artigo é descrever os aspectos metodológicos e principais desafios enfrentados para a execução do projeto COCASa, um estudo de coorte on-line sobre comportamento alimentar e saúde mental de docentes e discentes de IES do Brasil. O estudo foi iniciado em julho de 2020 e acompanhará os participantes por dois anos. Adotou-se amostragem não probabilística estratificada proporcional com a utilização de escalas, de inquérito alimentar e de questões estruturadas elaboradas pela equipe do projeto. Entre os participantes do baseline, 4.074 discentes e 2.210 docentes iniciaram o questionário e, respectivamente, 76,8% e 85,1% finalizaram o preenchimento. Em ambos os grupos, a maior participação foi de mulheres (docentes: 66,7% e discentes: 76,2%) e residentes nas regiões Nordeste (docentes: 37% e discentes: 50,9%) e Sul (docentes: 27,1% e discentes: 22,5%) do Brasil. A pesquisa on-line amplia a possibilidade de recrutamento de participantes e alcança limites territoriais com menor demanda por financiamento. Durante a pandemia da COVID-19, o uso do ambiente virtual tornou-se uma estratégia viável e acessível para a manutenção das atividades de pesquisa, configurando-se como uma provável tendência a ser adotada pela comunidade científica

Multicenter validation of PIM3 and PIM2 in Brazilian pediatric intensive care units

ObjectiveTo validate the PIM3 score in Brazilian PICUs and compare its performance with the PIM2.MethodsObservational, retrospective, multicenter study, including patients younger than 16 years old admitted consecutively from October 2013 to September 2019. We assessed the Standardized Mortality Ratio (SMR), the discrimination capability (using the area under the receiver operating characteristic curve – AUROC), and the calibration. To assess the calibration, we used the calibration belt, which is a curve that represents the correlation of predicted and observed values and their 95% Confidence Interval (CI) through all the risk ranges. We also analyzed the performance of both scores in three periods: 2013–2015, 2015–2017, and 2017–2019.Results41,541 patients from 22 PICUs were included. Most patients aged less than 24 months (58.4%) and were admitted for medical conditions (88.6%) (respiratory conditions = 53.8%). Invasive mechanical ventilation was used in 5.8%. The median PICU length of stay was three days (IQR, 2–5), and the observed mortality was 1.8% (763 deaths). The predicted mortality by PIM3 was 1.8% (SMR 1.00; 95% CI 0.94–1.08) and by PIM2 was 2.1% (SMR 0.90; 95% CI 0.83–0.96). Both scores had good discrimination (PIM3 AUROC = 0.88 and PIM2 AUROC = 0.89). In calibration analysis, both scores overestimated mortality in the 0%–3% risk range, PIM3 tended to underestimate mortality in medium-risk patients (9%–46% risk range), and PIM2 also overestimated mortality in high-risk patients (70%–100% mortality risk).ConclusionsBoth scores had a good discrimination ability but poor calibration in different ranges, which deteriorated over time in the population studied

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