The role of adversity and stress in psychopathology: some evidence and its implications for theory and research

Three lines of research--studies of extreme situations, epidemiological investigations of relations between socioeconomic status (SES) and psychiatric disorders, and a quasi-experimental test of the social causation-social selection issue raised by the epidemiological findings--provide strong evidence that environmental adversity is important in the occurrence not only of post-traumatic stress disorder (PTSD) but also of other types of psychopathology, including major depression, alcoholism, substance use disorders, antisocial personality disorder, and nonspecific distress. Leads from this triad of studies are developed into a basic proposition about the nature of the role of adversity and stress. The core of this proposition is that the likelihood of onset of the above types of disorder increases with two factors: (1) the proportion of the individual's usual activities in which uncontrollable negative changes take place following a major negative event; and (2) how central the uncontrollable changes are to the individual's important goals and values. The role of environmental adversity in bringing about these uncontrollable changes varies with gender, ethnic/racial status, and SES in our own and other modern, urban societies. The types of psychopathology that develop and their course vary with both the types of adversity (e.g., whether life threat is involved) and with the personal predispositions of the individuals who experience the adversity and stress (e.g., family history of particular types of psychopathology).     

Policy challenges for child welfare in the new century

Recent changes in public policy reflect increased efforts to scapegoat poor women and children and to polarize the objectives of child protection and family preservation. This article challenges various stakeholders in the child welfare field to move beyond the quick-fix solutions and bureaucratic overregulation precipitated by horror stories of child abuse in the media. The new century presents an opportunity to pursue differential intervention strategies that protect children while empowering poor families and communities.     

An interactive three-stage model for mutual funds portfolio selection

The aim of this work is to be a useful instrument for helping finance practitioners on the selection of suitable mutual fund portfolios. The portfolio selection problem is characterized by imprecision and/or vagueness inherent in the required data and more generally, in the context where investors have to make decisions. In order to mitigate these problems, a three stage model has been proposed based on a multi-index model and considering several market scenarios described in an imprecise way by an expert. The proposed fuzzy model allows the Decision Maker to select, by means of an outranking method, a suitable portfolio taking into account the uncertainty related to the market scenarios and the imprecision and/or vagueness associated with the model data.     

The need for revaluation and assessment of Australian foster care programmes

This is a brief report on one aspect of a programme of applied research, in a large social welfare agency, designed to provide a basis for the evaluation of the policies, activities and structure of the agency.     

A decomposition technique for quantifying real process performance

A decomposition technique for quantifying the impacts of changes in product mix and process performance on aggregate process-related indicators is presented. Through application of the technique, the real performance of a process can be quantified. Changes in real performance over time can be monitored to provide useful information for process evaluation and production planning. Two case studies, one related to the aggregate defective rate for an assembly line of an integrated circuit fabrication plant and the other to the aggregate inventory turnover for a tyre distribution company, are presented to illustrate the application of the technique.     

The estimation of derivatives of a nonparametric regression function when the data are correlated

A kernel estimator of a derivative of arbitrary order of a nonparametric average population curve is considered for a correlated-errors model with balanced replicate measurements at each design point. Asymptotic expansions of the mean squared error are derived for two classes of correlation functions in the model. Consistency, choice of smoothing parameter, and rates of convergence are examined for the important special cases of estimating the first and second derivatives.     

New Lilliefors and Srinivasan tables with applications

Two approaches to the problem of goodness-of-fit with nuisance parameters are presented in this paper, both based on modifications of the Kolmogorov-Smirnov statistics. Improved tables of critical values originally computed by Lilliefors and Srinivasan are presented in the normal and exponential cases. Also given are tables for the uniform case, normal with known mean and normal with known variance. All tables were computed using Monte Carlo simulation with sample size n = 20000.