Risk factors and correlates of dating violence: the relevance of examining frequency and severity levels in a college sample

To determine whether categorizing levels of violence along dimensions of frequency and severity would result in informative distinctions among individuals using dating violence, reported use of physical violence, along with variables theorized to be related to use of force in intimate relationships, was assessed in a sample of 617 college students (males = 290; females = 327). When participants' scores were analyzed by dichotomizing them along the lines of ever versus never using dating violence, numerous past findings were replicated. However, participants using a higher frequency of force were distinguished by needing to control their partner and by less inhibition in expressing their anger. Higher severity of force by an individual was predicted only by a need to control his/her dating partner. Implications for these findings were discussed.     

Illness career descent in institutions for the elderly

This paper examines the social psychological consequences of illness careers in a retirement facility. As elderly residents' health declines, they experience an illness career descent. This refers to the movement through the social structure of the healthcare facility as a consequence of the downward trajectory of chronic illness. Illness career descent requires the formal relocation of the individual and the informal social consequences of loss of control, stigmatization, and segregation from prior friendship networks. Individuals react in a three-stage manner. At first, residents resist being moved down. Next, there is a negative impact on their self-image as relocated residents accept the evaulation by staff that they have become less competent. Third, they adjust to their new location and enhance their self-image by developing new roles, finding a poor dear, re-evaluating prior stereotypes, and engaging in post-decisional dissonance reduction.The author wishes to express his deepest appreciation to Jacob Climo, Peter Conrad, Stan Kaplowitz, and Shulamit Reinharz for their encouragement and their suggestions on earlier drafts of this paper.     

Making Privatization Work: Utilizing a Scorecard Model of Human Resource Strategy

Three-fourths of privatization efforts fail. Analyses of the other one-fourth have overlooked what is arguably the single most important element: successful management of human resources. In establishing the culture of change necessary to effect competitive initiatives and in motivating employees toward innovation and accountability, a scorecard-based human resource strategy is at the center of it all. A discussion of the strategic role of human resource management supporting privatization efforts in general, and of the remarkably successful Charlotte, North Carolina experience in particular, offers important lessons. Detailed here is a rubric of what works and why.     

Balancing parents' and very young children's rights in care proceedings: decision-making in the context of the Human Rights Act 1998

This paper explores whether the Human Rights Act 1998 has influenced the approach a range of professionals, including social workers, managers and children's guardians adopt when they examine and seek to balance the potentially competing rights of parents and children involved in care proceedings. Drawing on findings from an empirical study of the decision‐making process that influences the life pathways of very young children in care, it also explores some of the dilemmas professionals face in their day‐to‐day practice and examines whether parental rights are prioritized over children's rights in certain instances.     

Model Averaging in Microbial Risk Assessment Using Fractional Polynomials

The alleviation of food-borne diseases caused by microbial pathogen remains a great concern in order to ensure the well-being of the general public. The relation between the ingested dose of organisms and the associated infection risk can be studied using dose-response models. Traditionally, a model selected according to a goodness-of-fit criterion has been used for making inferences. In this article, we propose a modified set of fractional polynomials as competitive dose-response models in risk assessment. The article not only shows instances where it is not obvious to single out one best model but also illustrates that model averaging can best circumvent this dilemma. The set of candidate models is chosen based on biological plausibility and rationale and the risk at a dose common to all these models estimated using the selected models and by averaging over all models using Akaike's weights. In addition to including parameter estimation inaccuracy, like in the case of a single selected model, model averaging accounts for the uncertainty arising from other competitive models. This leads to a better and more honest estimation of standard errors and construction of confidence intervals for risk estimates. The approach is illustrated for risk estimation at low dose levels based on Salmonella typhi and Campylobacter jejuni data sets in humans. Simulation studies indicate that model averaging has reduced bias, better precision, and also attains coverage probabilities that are closer to the 95% nominal level compared to best-fitting models according to Akaike information criterion.     

The Potential Impact of ‘Public Access’ Legislation on Access to Forestry Literature

There have been increasing calls for the United States (U.S.) government’s implementation of broad public access policies mandating free online access to federally funded research. This study examines the potential impact of such a policy on peer-reviewed forestry literature. The authors analyze information about federal government authorship, federal government funding, and U.S. authorship indicated in articles published in five core forestry journals in 2006. The results of the analysis provide evidence that federal public access legislation would have a significant impact on the accessibility of forestry literature published in leading journals in the field.     

Maximum likelihood from spatial random effects models via the stochastic approximation expectation maximization algorithm

We introduce a class of spatial random effects models that have Markov random fields (MRF) as latent processes. Calculating the maximum likelihood estimates of unknown parameters in SREs is extremely difficult, because the normalizing factors of MRFs and additional integrations from unobserved random effects are computationally prohibitive. We propose a stochastic approximation expectation-maximization (SAEM) algorithm to maximize the likelihood functions of spatial random effects models. The SAEM algorithm integrates recent improvements in stochastic approximation algorithms; it also includes components of the Newton-Raphson algorithm and the expectation-maximization (EM) gradient algorithm. The convergence of the SAEM algorithm is guaranteed under some mild conditions. We apply the SAEM algorithm to three examples that are representative of real-world applications: a state space model, a noisy Ising model, and segmenting magnetic resonance images (MRI) of the human brain. The SAEM algorithm gives satisfactory results in finding the maximum likelihood estimate of spatial random effects models in each of these instances.     

Lifetime Buy Decisions with Multiple Obsolete Parts

Life‐cycle mismatch occurs when the life cycles of parts end before the life cycles of the products in which those parts are used. Lifetime buys are one tactic for mitigating the effect of part obsolescence, where a quantity of parts is purchased for the remaining life of a product. We extend prior work that determines optimal lifetime buy quantities for one product with one obsolete part by providing an analytic solution and two simple heuristic policies for the optimal lifetime buy quantities when many parts become obsolete over a product's life cycle. We determine which of our two heuristics is most accurate for different product life cycles, which yields a metaheuristic with increased accuracy. That analysis also reveals critical perspectives in making lifetime buy decisions with nonstationary life‐cycle demand patterns.