Going social: Championing a holistic model of mental distress within professional education

Within the context of current debates as to how to raise the profile of social perspectives within mental health social work education, this paper seeks to assemble various elements that might belong within a social (or psychosocial) model of mental health practice. A clarification of what may actually be meant by a social model would be helpful in starting to set the parameters of future social work curricula, both at qualification and post-qualification levels, in terms of an appropriate value base and theoretical orientation. This may be seen to be important if social workers are to be properly prepared for taking on some form of leadership around the promotion of social approaches within the practice of multi-disciplinary teams.     

Leisure activity and adjustment in retirement

A sample of retired persons in a southern metropolitan area responded to a questionnaire designed to investigate the interrelationship between level of leisure activity and adjustment in retirement Adjustment was measured by two indices, life satisfaction and alienation. The survey's results show that persons who have more leisure activities are more satisfied with their lives and are less alienated than those who have few leisure activities.     

A DIRICHLET PROCESS MIXTURE OF HIDDEN MARKOV MODELS FOR PROTEIN STRUCTURE PREDICTION

By providing new insights into the distribution of a protein's torsion angles, recent statistical models for this data have pointed the way to more efficient methods for protein structure prediction. Most current approaches have concentrated on bivariate models at a single sequence position. There is, however, considerable value in simultaneously modeling angle pairs at multiple sequence positions in a protein. One area of application for such models is in structure prediction for the highly variable loop and turn regions. Such modeling is difficult due to the fact that the number of known protein structures available to estimate these torsion angle distributions is typically small. Furthermore, the data is "sparse" in that not all proteins have angle pairs at each sequence position. We propose a new semiparametric model for the joint distributions of angle pairs at multiple sequence positions. Our model accommodates sparse data by leveraging known information about the behavior of protein secondary structure. We demonstrate our technique by predicting the torsion angles in a loop from the globin fold family. Our results show that a template-based approach can now be successfully extended to modeling the notoriously difficult loop and turn regions.     

Universally beneficial manipulation: a characterization

There exist social choice rules for which every manipulation benefits everyone. This paper constructs a large variety of rules with this property and provides two characterizations of such rules.     

Pareto,anonymity, and independence: four alternatives

For four alternatives and an even number of individuals, we prove a conjecture in a companion paper: It is impossible for a social choice rule to satisfy all of (1) Pareto, (2) anonymity, (3) full domain, and (4) independence of some alternative, a relaxation of Arrow’s IIA.     

A comparative study of correlation coefficients in spatially MRSI-observed neurochemicals from multiple sclerosis patients

In measuring the association between magnetic resonance spectroscopic imaging (MRSI)-observed neurochemicals from multiple sclerosis (MS) patients, the classic correlation coefficients such as the Pearson's r, Spearman's ρand Kendall's τdo not take into account the spatial dependence of the observations. This paper reports a comparative study on these classic correlation coefficients (Pearson's r, Spearman's ρand Kendall's τ) and some more recent correlation coefficients (Tjostheim's a, the modified t) that take into account the spatial dependence of the intensities of the concentrations of several neurochemicals in MS patients. Our study indicates that the use of the classic correlation coefficients that ignores the spatial dependence of the observations may overestimate the statistical significance of the results.