Diversity Trouble

In line with Judith Butler’s famous “Gender Trouble”, this article starts by challenging some basic assumptions. What are the conceptual premises of diversity and its management? How do these concepts define identities? How do they decide and influence the entrepreneurial management of these identities? And what patterns of action are typical for diversity conscious organizations? Following these questions by focusing on the discussion of diversity in the German-speaking world, the authors discuss three distinct issues they have had with current diversity management concepts: first, the personalization of social systems; second, the monolithic construction of organizational cultures; and third, an understanding of change that follows a rather mechanistic logic. Trying to light up different blind spots, the article argues for a different approach to diversity management, paying more respect towards organizational dynamics, coping with complexity in a mindful manner, and a sense of ambivalence that should be at the heart of diversity leadership.     

Fat government,thin populace? Is the growth of obesity prevalence lower in more generous welfare states?

In the last few decades, the prevalence of obesity has expanded rapidly and now constitutes a grave public health concern across the developed world. Obesity is negatively associated with, among other factors, education and healthcare quality, which are factors that governments can and do influence through welfare policies. This study investigated to what extent the change in the prevalence of obesity is lower in more generous welfare states. Based on pooled time series cross‐section regressions for 15 developed countries over 25 years, we found that the more generous are transfer payments, health policy and education policy, the lower is the increase of obesity prevalence. These findings have important implications for public policy formation and the public health and welfare state literatures.     

A case-study in the clinical epidemiology of psoriatic arthritis: multistate models and causal arguments

In psoriatic arthritis, permanent joint damage characterizes disease progression and represents a major debilitating aspect of the disease. Understanding the process of joint damage will assist in the treatment and disease management of patients. Multistate models provide a means to examine patterns of disease, such as symmetric joint damage. Additionally, the link between damage and the dynamic course of disease activity (represented by joint swelling and stress pain) at both the individual joint level and otherwise can be represented within a correlated multistate model framework. Correlation is reflected through the use of random effects for progressive models and robust variance estimation for non-progressive models. Such analyses, undertaken with data from a large psoriatic arthritis cohort, are discussed and the extent to which they permit causal reasoning is considered. For this, emphasis is given to the use of the Bradford Hill criteria for causation in observational studies and the concept of local (in)dependence to capture the dynamic nature of the relationships.     

Spatial modelling of gene frequencies in the presence of undetectable alleles

Bayesian hierarchical models are developed to estimate the frequencies of the alleles at the HLA-C locus in the presence of non-identifiable alleles and possible spatial correlations in a large but sparse, spatially defined database from Papua New Guinea. Bayesian model selection methods are applied to investigate the effects of altitude and language on the genetic diversity of HLA-C alleles. The general model includes fixed altitudinal effects, random language effects and random spatially structured location effects. Conditional autoregressive priors are used to incorporate the geographical structure of the map, and Markov chain Monte Carlo simulation methods are applied for estimation and inference. The results show that HLA-C allele frequencies are explained more by linguistic than altitudinal differences, indicating that genetic diversity at this locus in Papua New Guinea probably tracks population movements and is less influenced by natural selection than is variation at HLA-A and HLA-B.     

Work, employment and mental health in Europe

In May 2004 the World Health Organization (WHO European Region) called a consultation meeting of its Collaborating Centres in Occupational Health to discuss mental health in the workplace. The meeting took place in Copenhagen. This paper presents an interpretation of the discussion that took place and the thinking that developed as a result. It was argued that increased interest and research and improved practice in the area of occupational mental health are required to promote good mental health in the European population. A framework of risk management and prevention based on sound evidence and good understanding could provide the basis for the translation of existing research knowledge into effective practice.     

Recent developments in exponential random graph (p*) models for social networks

This article reviews new specifications for exponential random graph models proposed by Snijders et al. [Snijders, T.A.B., Pattison, P., Robins, G.L., Handcock, M., 2006. New specifications for exponential random graph models. Sociological Methodology] and demonstrates their improvement over homogeneous Markov random graph models in fitting empirical network data. Not only do the new specifications show improvements in goodness of fit for various data sets, but they also help to avoid the problem of near-degeneracy that often afflicts the fitting of Markov random graph models in practice, particularly to network data exhibiting high levels of transitivity. The inclusion of a new higher order transitivity statistic allows estimation of parameters of exponential graph models for many (but not all) cases where it is impossible to estimate parameters of homogeneous Markov graph models. The new specifications were used to model a large number of classical small-scale network data sets and showed a dramatically better performance than Markov graph models. We also review three current programs for obtaining maximum likelihood estimates of model parameters and we compare these Monte Carlo maximum likelihood estimates with less accurate pseudo-likelihood estimates. Finally, we discuss whether homogeneous Markov random graph models may be superseded by the new specifications, and how additional elaborations may further improve model performance.