Multiproblem families: A social-psychological perspective

Summary It is generally conceded that our allied mental health professions have fallen short in our attempts to provide adequate services to those people whose need is most desperate, those individuals and families ambiguously and condescendingly designated multiproblem. It could be said that we, the caregivers, have been unwilling or unable to be good enough mothers to these, our motherless children. In fact, the early, formative experience of the people with whom we are concerned has invariably been marked by gross discontinuities in mothering; the consequent defects in controlling, regulatory psychic structure (ego defects) are most prominently manifested in the chaotic life-style which characterizes these individuals and families. Our task as caregivers, when viewed from this perspective, is clear: we must somehow bring order out of disorder. But, with tragic regularity, disorder prevails; the provider of service succumbs to the same painful feelings of disorganization, bewilderment, frustration, and helplessness with which his client struggles, and, eventually, both give up in despair. What is more, this sense of fragmentation and futility pervades the institutions responsible for provision of services. Client, caregiver, and institution, all are trapped in the same tortuous maze. How, then, are we to extricate ourselves? Certainly not by drafting yet another master plan which promises everything and delivers nothing. Both realistic and humanistic considerations dictate more modest goals: We might not be able to rescue everyone who needs assistance, but we can help a few. And, whatever approach we may adopt, the service we offer can only be effective if it is based upon a sustained and sustaining (in essence, maternal) relationship.Sometimes I Feel Like a Motherless Child     

The Big Five personality factors and team performance: implications for selecting successful product design teams

In the pursuit of faster product development, product design teams are a growing phenomenon in many organizations. In order to be successful, these teams must be composed of people who work well together. However, despite the benefit of selecting the optimal combination of team members, this topic has received little attention. Personality has been identified as a potentially helpful selection variable in the determination of optimal team composition. This study examines the relationships between the ‘Big Five’ personality factors (Conscientiousness, Extraversion, Neuroticism, Agreeableness, and Openness to Experience) and objective team performance for three-member product design teams. In addition to this, the potential incremental contribution of personality to the variance in team performance over that accounted for by established selection measures such as general cognitive ability was investigated. In the short duration of the study, it became apparent that some teams were capable of success, and some were not. Successful teams were characterized by higher levels of general cognitive ability, higher extraversion, higher agreeableness, and lower neuroticism than their unsuccessful counterparts. In successful teams, the heterogeneity of conscientiousness was negatively related to increments in product performance. Implications for the selection of product design teams and future directions for research are discussed.     

帕金森病患者住院费用相关因素分析

为分析并探讨帕金森病患者住院费用的影响因素,该研究选取2009年1月至2015年12月在南京医科大学第一附属医院帕金森病住院患者320例,其中接受脑深部电刺激器（DBS）置入手术患者37例,非手术患者283例,根据H&Y（Holehn &Yahr）分级、年龄以及合并感染情况等对住院费用及住院时间进行分析比较。结果发现,非手术组帕金森患者年龄、合并感染情况及H&Y分级与总住院费用及住院时间显著相关。接受DBS手术组患者住院费用较晚期非手术患者显著增高（t=7.40,P<0.001）。高龄、合并感染及帕金森病晚期增加非手术帕金森病患者住院费用和住院时间。     

On predictive distributions and Bayesian networks

In this paper we are interested in discrete prediction problems for a decision-theoretic setting, where the task is to compute the predictive distribution for a finite set of possible alternatives. This question is first addressed in a general Bayesian framework, where we consider a set of probability distributions defined by some parametric model class. Given a prior distribution on the model parameters and a set of sample data, one possible approach for determining a predictive distribution is to fix the parameters to the instantiation with the maximum a posteriori probability. A more accurate predictive distribution can be obtained by computing the evidence (marginal likelihood), i.e., the integral over all the individual parameter instantiations. As an alternative to these two approaches, we demonstrate how to use Rissanen's new definition of stochastic complexity for determining predictive distributions, and show how the evidence predictive distribution with Jeffrey's prior approaches the new stochastic complexity predictive distribution in the limit with increasing amount of sample data. To compare the alternative approaches in practice, each of the predictive distributions discussed is instantiated in the Bayesian network model family case. In particular, to determine Jeffrey's prior for this model family, we show how to compute the (expected) Fisher information matrix for a fixed but arbitrary Bayesian network structure. In the empirical part of the paper the predictive distributions are compared by using the simple tree-structured Naive Bayes model, which is used in the experiments for computational reasons. The experimentation with several public domain classification datasets suggest that the evidence approach produces the most accurate predictions in the log-score sense. The evidence-based methods are also quite robust in the sense that they predict surprisingly well even when only a small fraction of the full training set is used.     

On the Critical Value Behaviour of Multiple Decision Procedures

In this paper we investigate the asymptotic critical value behaviour of certain multiple decision procedures as e.g. simultaneous confidence intervals and simultaneous as well as stepwise multiple test procedures. Supposing that n hypotheses or parameters of interest are under consideration we investigate the critical value behaviour when n increases. More specifically, we answer e.g. the question by which amount the lengths of confidence intervals increase when an additional parameter is added to the statistical analysis. Furthermore, critical values of different multiple decision procedures as for instance step-down and step-up procedures will be compared. Some general theoretic results are derived and applied for various distributions.     

Asymptotic Bias in Parameter Estimation of AR-Processes Using Recursive Least Squares with Exponential Forgetting

The recursive least squares technique is often extended with exponential forgetting as a tool for parameter estimation in time-varying systems. The distribution of the resulting parameter estimates is, however, unknown when the forgetting factor is less than one. In this paper an approximative expression for bias of the recursively obtained parameter estimates in a time-invariant AR( n_a ) process with arbitrary noise is given, showing that the bias is non-zero and giving bounds on the approximation errors. Simulations confirm the approximation expressions.     

Effects of sterile insect releases on a population under predation or parasitism

Summary A continuous-time differential equation model was constructed which describes the population dynamics of a predator prey system in which sterile prey are released in a program designed to eradicate or reduce the prey population. It was found that the dynamics of the system behave quite differently when predators are present. Two conditions were found which have differing implications for the control program. If the predators still exist when the wild prey population declines to extinction, then the SIRM is assisted by the predators, sometimes to a considereble extent. If the predators decline to extinction before the wild prey population goes extinct, then the predators may or may not assist the SIRM depending on the parameters of the system. If the predators do assist the SIRM, then a potentially dangerous situation exists in which an explosion of the prey population could occur after the predators go extinct. Predator polyphagy would probably minimize this danger of an explosion since it would stabilize the predator population.