The Nursing Home as Total Institution: A Case Study and Suggestions for the Aged Care System

A symbolic interactionist perspective is used to examine the effects of institutionalization on residents of a Sydney nursing home. The analysis suggests that much of the ‘negative’ behaviour observed among inmates results from the institutional structuring of a dependence role. Implications for long term care of the aged in both community and institutional settings are discussed.     

Ordinal equivalence of power notions in voting games

In this paper, we are concerned with the preorderings (SS) and (BC) induced in the set of players of a simple game by the Shapley–Shubik and the Banzhaf–Coleman's indices, respectively. Our main result is a generalization of Tomiyama's 1987 result on ordinal power equivalence in simple games; more precisely, we obtain a characterization of the simple games for which the (SS) and the (BC) preorderings coincide with the desirability preordering (T), a concept introduced by Isbell (1958), and recently reconsidered by Taylor (1995): this happens if and only if the game is swap robust, a concept introduced by Taylor and Zwicker (1993). Since any weighted majority game is swap robust, our result is therefore a generalization of Tomiyama's. Other results obtained in this paper say that the desirability relation keeps itself in all the veto-holder extensions of any simple game, and so does the (SS) preordering in all the veto-holder extensions of any swap robust simple game.     

Application domains for the Delta method

The Delta method uses truncated Lagrange expansions of statistics to obtain approximations to their distributions. In this paper, we consider statistics Y=g(μ+X), where X is any random vector. We obtain domains 𝒟 such that, when μ∈𝒟, we may apply the distribution derived from the Delta method. Namely, we will consider an application on the normal case to illustrate our approach.     

Chisquared and related inducing pivot variables: an application to orthogonal mixed models

Abstract

We use chi-squared and related pivot variables to induce probability measures for model parameters, obtaining some results that will be useful on the induced densities. As illustration we considered mixed models with balanced cross nesting and used the algebraic structure to derive confidence intervals for the variance components. A numerical application is presented.     

Panning for gold: a clinician's guide to using research

The gap between research and clinical practice is one of the key challenges facing family therapy. Clinicians often fail to incorporate research findings into their practice because they do not know how to search, evaluate, or apply research to their clinical work. The purpose of this article is to help clinicians become better consumers of research. This article explores the potential value of research to clinicians, as well as negative beliefs that clinicians may have about research. The article also describes how clinicians can use research to inform their clinical work, as well as potential challenges that can be encountered.     

Voice and Agency in Social Movement Outcomes

Focusing on the interactional dynamics of movements, we find that two constructs, voice and agency are critical to the development of a sense of “groupness” and can aid social movement actors in accomplishing desired goals. Voice and agency are accomplished when movement actors engage in various processes such as planning and strategizing, completion of goal-oriented tasks and other unifying activities. We examine four social movement organizations operating in separate movement contexts with different outcomes: contested gay politics in Cincinnati, Ohio and grassroots feminism in Cleveland, Ohio and New York City, New York. We find that groups will have a better chance at achieving their goals if members are able to create a unified voice, and if leaders include and draw from the strengths of those they recruit, thus allowing a sense of agency.

Maximum-Entropy Prior Uncertainty and Correlation of Statistical Economic Data

Empirical estimates of source statistical economic data such as trade flows, greenhouse gas emissions, or employment figures are always subject to uncertainty (stemming from measurement errors or confidentiality) but information concerning that uncertainty is often missing. This article uses concepts from Bayesian inference and the maximum entropy principle to estimate the prior probability distribution, uncertainty, and correlations of source data when such information is not explicitly provided. In the absence of additional information, an isolated datum is described by a truncated Gaussian distribution, and if an uncertainty estimate is missing, its prior equals the best guess. When the sum of a set of disaggregate data is constrained to match an aggregate datum, it is possible to determine the prior correlations among disaggregate data. If aggregate uncertainty is missing, all prior correlations are positive. If aggregate uncertainty is available, prior correlations can be either all positive, all negative, or a mix of both. An empirical example is presented, which reports relative uncertainties and correlation priors for the County Business Patterns database. In this example, relative uncertainties range from 1% to 80% and 20% of data pairs exhibit correlations below ?0.9 or above 0.9. Supplementary materials for this article are available online.     

A weakly informative prior for Bayesian dynamic model selection with applications in fMRI

In recent years, Bayesian statistics methods in neuroscience have been showing important advances. In particular, detection of brain signals for studying the complexity of the brain is an active area of research. Functional magnetic resonance imagining (fMRI) is an important tool to determine which parts of the brain are activated by different types of physical behavior. According to recent results, there is evidence that the values of the connectivity brain signal parameters are close to zero and due to the nature of time series fMRI data with high-frequency behavior, Bayesian dynamic models for identifying sparsity are indeed far-reaching. We propose a multivariate Bayesian dynamic approach for model selection and shrinkage estimation of the connectivity parameters. We describe the coupling or lead-lag between any pair of regions by using mixture priors for the connectivity parameters and propose a new weakly informative default prior for the state variances. This framework produces one-step-ahead proper posterior predictive results and induces shrinkage and robustness suitable for fMRI data in the presence of sparsity. To explore the performance of the proposed methodology, we present simulation studies and an application to functional magnetic resonance imaging data.     

The Rate of Convergence of some Asymptotic Expansions for Distribution Approximations via an Esseen Type Estimate

Some asymptotic expansions not necessarily related to the central limit theorem are studied. We first observe that the smoothing inequality of Esseen implies the proximity, in the Kolmogorov distance sense, of the distributions of the random variables of two random sequences satisfying a sort of general asymptotic relation. We then present several instances of this observation. A first example, partially motivated by the the statistical theory of high precision measurements, is given by a uniform asymptotic approximation to (g(X + μ _n )) _n∈?, where g is some smooth function, X is a random variable and (μ _n ) _n∈? is a sequence going to infinity; a multivariate version is also stated and proved. We finally present a second class of examples given by a randomization of the interesting parameter in some classical asymptotic formulas; namely, a generic Laplace's type integral, randomized by the sequence (μ _n X) _n∈?, X being a Gamma distributed random variable.     

Influence of habitat type and distance from source area on bird taxonomic and functional diversity in a Neotropical megacity

The Neotropical region has been subjected to massive urbanization, which poses high risks for some global biodiversity hotspots and losses of ecosystem functions and services. In this study, we investigate how distance from large patches of native forests (source areas) and vegetation (green)/and infrastructure (gray) characteristics affect bird species richness and functional diversity in São Paulo megacity, southeastern Brazil. We analyzed the effects of source areas and green/gray characteristics on species richness and functional diversity (richness, evenness, and divergence) indices. We detected 231 bird species, and our data confirmed our predictions: (1) bird species richness in urbanized habitats was found to be (~?50–85%) lower than in source habitats; (2) species richness and trait composition significantly decreased as the distance from the source area increased, while functional richness was not affected by this metric; and (3) shrub and herbaceous covers and maximum height of trees were positively correlated with species richness and unique functional traits regarding habitat, diet, foraging and nesting strata and dispersal ability of birds in the forest-urban matrix. The number of buildings was negatively correlated with bird species richness and functional richness. Maximum height of buildings caused dramatic declines in functional evenness. Functional divergence was notably lower in sites with high shrub cover. Our study stresses the complexity of vegetation embedded in large Neotropical urban settlements and the need to maintain large protected areas surrounding megacities to mitigate the impacts of urbanization on birds.