Mediating factors explain anxiety experienced by women with obesity during the Covid-19 pandemic

The Covid-19 pandemic could be a source of great anxiety, especially for those at higher risk, such as women experiencing obesity. The aim of this study was to measure how some personal characteristics such as BMI (from underweight to class 3 obesity), bariatric surgery (yes or no), comorbidities, or age (as antecedent variables), and mediating factors impacted state anxiety during the Covid-19 Pandemic. Mediating factors were related to subjective knowledge or attitudes (e.g. interest or beliefs and practices around Covid-19, subjective health perception, and confidence in the government). French women (N = 532) were invited to take part in a voluntary online health survey during lockdown in Paris and its suburbs. Results showed that women with higher BMI had higher anxiety scores, primarily because they feel less healthy than other people. Secondly, the larger the body size of the participants was (BMI), the less they reported that information about Covid-19 held their attention. This lack of interest resulted in feelings of anxiety not being generated. Thirdly, the larger their body size was, the less confidence they had in the effectiveness of the proposed measures by the government and therefore, the more anxious they were. Finally, older age predicted higher interest in the pandemic, higher subjective health, and higher confidence in the government. Identifying obesity as a potential risk factor for anxiety disorders is crucial, but measuring the relationship between state anxiety and personal characteristics (e.g. BMI) requires considering mediating variables (e.g. subjective health perception). To reduce anxiety in women with obesity, it appears necessary to focus on psychological programs that can help them improve their perception of their health, as well as the confidence they may have in institutions, especially for younger women

Women with obesity are not as curvy as they think: consequences on their everyday life behavior

Two studies explore the impact of body size on daily life activities of women with obesity. In the first study, ethnographic techniques (first-person perspective video recordings) and subsequent interviews based on the video recordings were used. Results showed atypical behavior of women with obesity and ex-obese women related to memories of embarrassing experiences regarding personal body size (sitting, passing doors sideways, over-careful navigation in public space, and choosing clothes sizes too large.) Women with obesity seem to behave as if they thought they had a larger body than it actually was. These atypical behaviors are related to memories of embarrassing experiences regarding personal body size and stigma. Overweight women exhibit the same behavior but to a lesser and less systematic degree. In the second study, the represented (imagined) body size was compared to the perceived (in a mirror) body size with digital morphing techniques. In the mirror condition, the perceived image is accurate, while in the absence of a mirror women with obesity overestimate their body size by about 30%. Moreover, overestimation of imagined body size increased according to the weight status. Finally, women who had bariatric surgery had poorer estimates than women who had not. This would result of being continuously reminded of obesity and its stigma by daily embarrassing experiences, by being confronted with an environment designed for normal weight (e.g., narrow seats, turnstiles etc.) that makes obesity salient. We suggest that body size overestimation is a case of accentuation where things that matter are perceived bigger. These results could also been explained by the allocentric lock theory

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

Conception et évaluation, selon une démarche d'ingénierie psychosociale, d'un outil de recueil d'informations concernant l'apparence physique des personnes

La thèse est consacrée au contrôle des élèves au collège, c'est à dire au contrôle du comportement ordinaire des élèves ainsi qu'au traitement des comportements déviants et/ou perturbateurs. Ainsi entendu, le contrôle des élèves est envisagé comme une composante du travail des agents et des catégories d'agents des collèges (chefs d'établissement, conseillers d'éducation, enseignants, surveillants et aides éducateurs). La recherche analyse quelles sont les conditions de ce travail et à quelles organisation et pratiques il donne lieu. Elle repose sur l'e tude de quatre collèges ayant fait l'objet d'une enquête de terrain. Le recrutement respectif de ces établissements (populaire, intermédiaire ou favorisé) est un des principes de variation essentiels. S'appuyant sur une distinction entre le contrôle des élèves hors cours et en cours, la thèse montre en particulier que chaque catégorie d'agents admet que sa situation de travail comporte une part de travail de contrôle des élèves, tout en attendant des autres catégories un travail de contrôle précis.ST DENIS-BU PARIS8 (930662101) / SudocSudocFranceF