Religiosity, denominational affiliation and sexual behaviors among people with HIV in the United States

This study sought to describe religiosity and denominational affiliation among the U.S. population living with HIV and to test whether either is associated with HIV-related sexual risk behaviors. A nationally representative sample of 1,421 people in care for HIV, 932 of whom reported recent sexual activity, was utilized. Religiosity was associated with fewer sexual partners and a lower likelihood of engaging in unprotected sex and in high-risk sex. Catholics were less likely to report unprotected sex than were other Christians, adherents of non-Christian religions, and those reporting no religious affiliation. Catholics were also less likely than other Christians to report high-risk sex and reported fewer sexual partners compared to those of non-Christian religions. We did not observe a difference between Catholics and Evangelicals in the three sexual behaviors investigated. Results suggest that religiosity and some religious teachings may promote safer sex among people with HIV.     

Lean-healthcare approach to reduce costs in a sterilization plant based on surgical tray rationalization

Abstract

Sterilization plants process all reusable materials used in hospitals during different stages of healthcare provision. Most items sterilized come from the surgical theater in the form of individual instruments, linen and surgical trays. Reducing the number of instruments in trays impacts on the performance of the sterilization process. Here, we propose a three-phase method, based on lean healthcare principles, cluster analysis and kaizen groups of experts to rationalize surgical trays, reducing sterilization processing costs. We innovate by proposing a strategy to approach tray rationalization prioritizing surgical specialties that are both complex and frequent, and by using group technology to cluster surgical trays expediting expert analyses carried out by kaizen groups. Our propositions were tested in a high complexity, tertiary care hospital, covering 12 surgical specialties, reducing the number of instruments by average 9.75%, and the time-to-assemble trays by average 9.68%, obtaining annual savings of US$285.756,00 in sterilization processing costs.     

CSR committees on boards: the impact of the external country level factors

Journal of Management and Governance - This article explores the spread of corporate social responsibility (CSR) committees in the European countries, examining whether and what country level...     

Testing for additivity and joint effects in multivariate nonparametric regression using Fourier and wavelet methods

We consider the problem of testing for additivity and joint effects in multivariate nonparametric regression when the data are modelled as observations of an unknown response function observed on a d-dimensional (d 2) lattice and contaminated with additive Gaussian noise. We propose tests for additivity and joint effects, appropriate for both homogeneous and inhomogeneous response functions, using the particular structure of the data expanded in tensor product Fourier or wavelet bases studied recently by Amato and Antoniadis (2001) and Amato, Antoniadis and De Feis (2002). The corresponding tests are constructed by applying the adaptive Neyman truncation and wavelet thresholding procedures of Fan (1996), for testing a high-dimensional Gaussian mean, to the resulting empirical Fourier and wavelet coefficients. As a consequence, asymptotic normality of the proposed test statistics under the null hypothesis and lower bounds of the corresponding powers under a specific alternative are derived. We use several simulated examples to illustrate the performance of the proposed tests, and we make comparisons with other tests available in the literature.     

Applying multivariate techniques to high-dimensional temporally correlated fMRI data

In first-level analyses of functional magnetic resonance imaging data, adjustments for temporal correlation as a Satterthwaite approximation or a prewhitening method are usually implemented in the univariate model to keep the nominal test level. In doing so, the temporal correlation structure of the data is estimated, assuming an autoregressive process of order one.We show that this is applicable in multivariate approaches too - more precisely in the so-called stabilized multivariate test statistics. Furthermore, we propose a block-wise permutation method including a random shift that renders an approximation of the temporal correlation structure unnecessary but also approximately keeps the nominal test level in spite of the dependence of sample elements.Although the intentions are different, a comparison of the multivariate methods with the multiple ones shows that the global approach may achieve advantages if applied to suitable regions of interest. This is illustrated using an example from fMRI studies.     

Markov random field-based image subsampling method

The use of Bayesian models for the reconstruction of images degraded by both some blurring function H and the presence of noise has become popular in recent years. Making an analogy between classical degradation processes and resampling, we propose a Bayesian model for generating finer resolution images. The approach involves defining resampling, or aggregation, as a linear operator applied to an original picture to produce derived lower resolution data which represent our available experimental infor-mation. Within this framework, the operation of making inference on the orginal data can be viewed as an inverse linear transformation problem. This problem, formalized through Bayes' theorem, can be solved by the classical maximum a posteriori estimation procedure. Image local characteristics are assumed to follow a Gaussian Markov random field. Under some mild assumptions, simple, iterative and local operations are involved, making parallel 'relaxation' processing feasible. experimental results are shown on some images, for which good subsampling estimates are obtained.     

Roundoff error for floating point representation of real data

In the present paper we define aprobabilistic model for the study ofthe relative roundoff error in the floating point representation ofreal data. In particular, starting from the assumption that real data is uniformly distributed in the interval (0,1), each floating point is assigned aprobability value corresponding to the proportion of real data assigned this floating point value. For each real number x～ U(0,1), we compute the relative roundoff error in the floating point representation and we calculate i t s expected value and variance.     

JEEA‐FBBVA LECTURE 2013: THE NEW EMPIRICAL ECONOMICS OF MANAGEMENT

Over the last decade the World Management Survey (WMS) has collected firm‐level management practices data across multiple sectors and countries. We developed the survey to try to explain the large and persistent total factor productivity (TFP) differences across firms and countries. This review paper discusses what has been learned empirically and theoretically from the WMS and other recent work on management practices. Our preliminary results suggest that about a quarter of cross‐country and within‐country TFP gaps can be accounted for by management practices. Management seems to matter both qualitatively and quantitatively for performance at the level of the firm and the nation. Competition, governance, human capital, and informational frictions help account for the variation in management. We make some suggestions for both policy and future research.     

Relational Equity as a Design Tool Within Making and Tinkering Activities

This article focuses on the partner-like relations that emerge between undergraduates and youth as they engage in “Making and Tinkering” activities in an afterschool learning ecology, and illustrates the potential for designed tinkering activity to produce relational equity among participants. Grounded in sociocultural theory, but leveraging theoretical contributions from learning sciences and tinkering research, we draw on ethnographic data across one year to examine how the social organization of Making & Tinkering activities provides necessary social conditions for “feedback-in-practice” and consequential learning. Analyses of interactions reveal how more symmetrical intergenerational relationships serve in the design of equitable learning spaces.