Aging With a Severe Mental Illness: Challenges and Treatments

Severe mental illness (SMI) impacts many different facets of individuals' lives. As they age, those with SMI experience increased physical illnesses, functional impairment, cognitive deficits, and social disability. To promote more effective social work practice with this population, this article highlights the major biopsychosocial and family issues of aging with a psychiatric diagnosis. In addition, intervention approaches that have demonstrated efficacy are summarized along with ways to coordinate services across multiple service delivery sectors. Finally, future directions in practice and research are presented to develop additional methods of support for older adults with SMI and their families.     

Ballot Design and Unrecorded Votes on Paper-Based Ballots

The 2000 presidential election focused attention on the problemof unrecorded votes, in which a person casts a ballot but failsto record a valid vote for a particular contest. Although muchrecent research has evaluated voting technologies and theireffects on unrecorded votes, there has been little researchon the effects of ballot design. We argue that the same theoriesused to design and evaluate self-administered surveys can beused to analyze ballot features. We collect and code paper-basedballots used in the 2002 general election from 250 countiesin 5 states. We code the ballots in terms of several graphicdesign elements, including the content and location of ballotinstructions and the layout of candidate names and office titles.Our analysis suggests that several ballot features are associatedwith unrecorded votes (both overvotes and undervotes) in thegubernatorial contests. We also find that ballot design featuresexacerbate the racial disparity in unrecorded votes. Ballotdesign can be an important factor in determining whether votersare able to cast a ballot accurately, which can influence thelegitimacy of elections.     

Minimax strategies in survey sampling

The risk of a sampling strategy is a function on the parameter space, which is the set of all vectors composed of possible values of the variable of interest. It seems natural to ask for a minimax strategy, minimizing the maximal risk. So far answers have been provided for completely symmetric parameter spaces. Results available for more general spaces refer to sample size 1 or to large sample sizes allowing for asymptotic approximation. In the present paper we consider arbitrary sample sizes, derive a lower bound for the maximal risk under very weak conditions and obtain minimax strategies for a large class of parameter spaces. Our results do not apply to parameter spaces with strong deviations from symmetry. For such spaces a minimax strategy will prescribe to consider only a small number of samples and takes a non-random and purposive character, which is in accordance with the common practice of completely sampling a stratum of large units.     

The role of ostensive and performative routine aspects in dynamic capability deployment at different organizational levels

This study clarifies how dynamic capabilities work in modifying operational capabilities at different organizational levels. While there is good understanding that routines that make up operational capabilities possess ostensive and performative aspects, whether the same applies to dynamic capabilities is unclear. In addition, there is only a limited understanding of how dynamic capabilities link to operational capabilities in terms of these two routine aspects, in general, and at different organizational levels, in particular. Our findings suggest that ostensive and performative routine aspects explain the way in which dynamic capabilities work in modifying operational capabilities. They also reveal that the pattern of the ostensive or performative routine aspects in dynamic capability deployment at a selected organizational level correspond with that associated with operational capabilities at the same level.     

Great project management=IT success

Discover how creating a formal project management office can help a health care organization achieve on-time information technology projects that stay within their budgets.     

Parameter Estimation Algorithms for the Hyper-Gamma Distribution Class

Microcomputer-based algorithms for the estimation of the parameters shift, scale, initial and terminal shape of the hyper–Gamma distribution class are presented. They are based on the moment equations and on the logarithmic likelihood function (LLF) associated with the hyper-Gamma density. The maximum–likelihood approach is implemented by means of the derivative equations resulting from the LLF and, independently, by means of direct optimization of the LLF. Program options include estimation of (i) four parameters, (ii) three parameters (shift known), and (iii) two parameters (shift known, initial shape zero). A program diskette with user's guide will be made available upon request.