Patterns of Social Support in the Middle Childhood to Early Adolescent Transition: Implications for Adjustment

Children's social networks often include close family members, extended family members, and friends, but little is known about interindividual differences in the patterning of support from these sources. In this study, we used person‐oriented analyses to differentiate patterns of support for children undergoing the transition to adolescence. Social network and adjustment data were collected for an ethnically diverse sample of 691 fourth‐ and sixth‐grade children, with a two‐year follow‐up. Cluster analyses identified one pattern of support from both close family members and friends, a second pattern of support from both close and extended family members, and a third pattern of support primarily from close family members alone. Participants receiving support from close family in combination with either extended family or friends were better adjusted than were those without multiple support resources. The results suggest that a meaningful typology of children's social networks can be developed.     

Decision‐Making Analytics Using Plural Resilience Parameters for Adaptive Management of Complex Systems

It is critical for complex systems to effectively recover, adapt, and reorganize after system disruptions. Common approaches for evaluating system resilience typically study single measures of performance at one time, such as with a single resilience curve. However, multiple measures of performance are needed for complex systems that involve many components, functions, and noncommensurate valuations of performance. Hence, this article presents a framework for: (1) modeling resilience for complex systems with competing measures of performance, and (2) modeling decision making for investing in these systems using multiple stakeholder perspectives and multicriteria decision analysis. This resilience framework, which is described and demonstrated in this article via a real‐world case study, will be of interest to managers of complex systems, such as supply chains and large‐scale infrastructure networks.     

Separating thoughts from the self by selling them to others: the moderating role of self-esteem / Separar los pensamientos del yo al venderlos a otros: el rol moderador de la autoestima

Abstract

This research introduces a new approach for separating people from their thoughts by anticipating selling them to others. Participants were asked to write down either positive or negative thoughts about fast food on different pieces of paper. Then, participants were randomly assigned to role-play the part of either potential buyers or sellers for an advertising campaign. Finally, all participants indicated their self-esteem (as an additional predictor) and their attitudes towards fast food (as the dependent measure). For high self-esteem participants, anticipating selling thoughts to others reduced the impact of thoughts on attitudes, suggesting that distancing thoughts from a location with high validity (the self) reduced the perceived value of their thoughts. In contrast, for low self-esteem participants, anticipating selling thoughts increased their usage, suggesting that separating thoughts from a location with low validity (the self) increased the perceived value of those thoughts. These findings suggest that thoughts can be associated with high or low validity depending on individual differences in self-esteem.     

Extending Integrated Nested Laplace Approximation to a Class of Near‐Gaussian Latent Models

This work extends the integrated nested Laplace approximation (INLA) method to latent models outside the scope of latent Gaussian models, where independent components of the latent field can have a near‐Gaussian distribution. The proposed methodology is an essential component of a bigger project that aims to extend the R package INLA in order to allow the user to add flexibility and challenge the Gaussian assumptions of some of the model components in a straightforward and intuitive way. Our approach is applied to two examples, and the results are compared with that obtained by Markov chain Monte Carlo, showing similar accuracy with only a small fraction of computational time. Implementation of the proposed extension is available in the R‐INLA package.     

EM-Type algorithms for heavy-tailed logistic mixed models

This paper aims at evaluating different aspects of Monte Carlo expectation – maximization algorithm to estimate heavy-tailed mixed logistic regression (MLR) models. As a novelty it also proposes a multiple chain Gibbs sampler to generate of the latent variables distributions thus obtaining independent samples. In heavy-tailed MLR models, the analytical forms of the full conditional distributions for the random effects are unknown. Four different Metropolis–Hastings algorithms are assumed to generate from them. We also discuss stopping rules in order to obtain more efficient algorithms in heavy-tailed MLR models. The algorithms are compared through the analysis of simulated and Ascaris Suum data.     

Influence Measures in Quantile Regression Models

In this article, we use the asymmetric Laplace distribution to define a new method to determine the influence of a certain observation in the fit of quantile regression models. Our measure is based on the likelihood displacement function and we propose two types of measures in order to determine influential observations in a set of conditional quantiles conjointly or in each conditional quantile of interest. We verify the validity of our average measure in a simulated data set as well in an illustrative example with data about air pollution.     

Known Mean,Unknown Maxima? Testing the Maximum Knowing Only the Mean

In the quantitative group testing problem, the use of the group mean to identify if the group maximum is greater than a prefixed threshold (infected group) is analyzed, using n independent and identically distributed individuals. Under these conditions, it is shown that the information of the mean is sufficient to classify each group as infected or healthy with low probability of misclassification when the underline distribution is a unilateral heavy-tailed distribution.     

Native and exotic species in the urban landscape of the city of Rio de Janeiro,Brazil: density,richness, and arboreal deficit

Shaped by European influences, the Brazilian urban landscape was marked by the predominant use of exotic species in planted areas. In the 19th century along the city streets of Rio de Janeiro, trees were planted at a standard distance from each other. With time, native species began to be used in the urban landscape. Our purpose was to evaluate the utilization of exotic plant species in the urban landscape of Rio de Janeiro, evaluate the city’s arboreal deficit, and relate its neighborhood arboreal density to its economic index. Arboreal deficit represents the negative difference between the total number of expected trees and the observed number in the streets based in the standard distance used in the past. Twenty native and 40 exotic species were found in the 1701 streets sampled. A high percent of streets did not have any planted trees and the number of trees was greater in wealthier neighborhoods. The strong prevalence of exotic species is indicative of the negative impact of human activity on the biota. Since the municipality of Rio de Janeiro harbors forested areas, the risk for dissemination of exotic species is high.     

Unveiling Research Trends for Organizational Reputation in the Nonprofit Sector

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations - Public scrutiny and the need for funds in a more competitive environment are pressuring nonprofits to be more consciously...     

An examination of ecological predictors of health literacy in black college students

Objective: Health literacy is a determinant of health, but disparities in health literacy persist. This study examined the influence of ecological factors on college students’ health literacy. Participants: During January 2016 a nonrandom sample of black undergraduate students (n = 298) aged 18–24 were recruited from enrollment lists at two urban universities in the Southeastern United States. Methods: Information on health literacy as well as numerous intrapersonal, social, and cultural-environment factors was obtained using an electronic questionnaire and then statistically modeled. Results: Ecological factors accounted for 28.7% of the variance in health literacy. In particular, reappraisal (B = 0.323, p < .001), suppression (B = ?0.289, p < .001), campus health education (B = 0.192, p < .05), campus tobacco culture (B = ?0.174, p < .05), and perceived norms (B = ?0.153, p < .05) directly predicted health literacy. Conclusions: Although intrapersonal factors influence health literacy, the sociocultural environment of college can also foster or hinder college students’ health literacy.