Maternal Regulation of Daughters' Emotion During Conflicts From Early to Mid‐Adolescence

Primary caregivers play an important role in emotion socialization. Real‐time mother–daughter emotion socialization was examined in 45 mother–daughter dyads with early‐adolescent daughters (age M = 11.80, SD = .27) at the first observation point. Maternal supportive emotion regulation and daughters' emotions were coded during two conflict discussions, 2 years apart. With multilevel survival analysis, the likelihood of maternal supportiveness was predicted both over time, between early and mid‐adolescence, and by daughters' pubertal status. Mothers were more likely to respond to daughters' negative and positive emotions with supportiveness for daughters whose pubertal maturation occurred relatively early. Results suggest that mothers adjust their socialization of daughters' emotions according to their daughters' pubertal development.     

Psychosocial Profile of Children Having Participated in an Intervention Program for Their Sexual Behavior Problems: The Predictor Role of Maltreatment

This study sought to verify if a history of maltreatment may predict the psychosocial profile of children who participated in an intervention program aiming at reducing sexual behavior problems. Data were collected at both the beginning and the end of the intervention program using a clinical protocol and standardized tests selected on the basis of the intervention targets. In general, the results indicate that children who had experienced maltreatment display a psychosocial profile that is similar to that of children who had not experienced maltreatment. However, children who had experienced psychological abuse or neglect may display greater externalized or sexualized behaviors, whereas children who have a parent who had been a victim of sexual abuse may display fewer sexualized behaviors.     

Contextual Effects on Surprise Expressions: A Developmental Study

Although research succeeds in eliciting spontaneous feelings of surprise with participants, these participants rarely show a prototypical expression of raising eyebrows, opening mouth, and widening eyes. In other words, there seems to be a discrepancy between the display and feeling of surprise. To get a better understanding of this discrepancy, we assessed what factors influence the display of surprise in children (study 1) and adults (study 2). In both studies, we conducted a quiz-like experiment, in which we manipulated the social context (participants either competed or collaborated), and various quiz questions to extract reactions of surprise (either caused by unexpectedly correct or unexpectedly incorrect answers). Results show that cause and social context did not affect the appearance of specific features in participants’ surprise display. However, we did find these factors to interact with regards to the intensity of perceived surprise displays of adults. For children, these relations were less complex. Overall, we can conclude that the expression of surprise is indeed moderated by contextual factors, namely cause of the surprise, social context, and age.     

Are We at the Cutting Edge or the Blunt Edge?: Improving NGO Organizational Performance with Private and Public Sector Strategic Management Frameworks

Bankruptcy or irrelevance are likely paths for not‐for‐profit organizations (NGOs) that do not reshape themselves to achieve greater impact, efficiency, and accountability in an increasingly global, competitive environment. But how relevant are public and private sector management frameworks for NGO repositioning? This article provides initial answers through a case study of CARE, one of the largest international relief and development nonprofit organizations in the United States. It explores the transformation frameworks employed at CARE between 1992 and 1997, assesses what worked and what did not, and identifies initial lessons learned for the broader sector.     

Bayesian calibration of computer models

We consider prediction and uncertainty analysis for systems which are approximated using complex mathematical models. Such models, implemented as computer codes, are often generic in the sense that by a suitable choice of some of the model's input parameters the code can be used to predict the behaviour of the system in a variety of specific applications. However, in any specific application the values of necessary parameters may be unknown. In this case, physical observations of the system in the specific context are used to learn about the unknown parameters. The process of fitting the model to the observed data by adjusting the parameters is known as calibration. Calibration is typically effected by ad hoc fitting, and after calibration the model is used, with the fitted input values, to predict the future behaviour of the system. We present a Bayesian calibration technique which improves on this traditional approach in two respects. First, the predictions allow for all sources of uncertainty, including the remaining uncertainty over the fitted parameters. Second, they attempt to correct for any inadequacy of the model which is revealed by a discrepancy between the observed data and the model predictions from even the best-fitting parameter values. The method is illustrated by using data from a nuclear radiation release at Tomsk, and from a more complex simulated nuclear accident exercise.     

Revealing age-specific past and future unrelated costs of pneumococcal infections by flexible generalized estimating equations

We aimed to study the excess health-care expenditures for persons with a known positive isolate of Streptococcus pneumoniae. The data set was compiled by linking the database of the largest Belgian Sickness Fund with data obtained from laboratories reporting pneumococcal isolates. We analyzed the age-specific per-patient cumulative costs over time, using generalized estimating equations (GEEs). The mean structure was described by fractional polynomials. The quasi-likelihood under the independence model criterion was used to compare different correlation structures. We show for all age groups that the health-care costs incurred by diagnosed pneumococcal patients are significantly larger than those incurred by undiagnosed matched persons. This is not only the case at the time of diagnosis but also long before and after the time of diagnosis. These findings can be informative for the current debate on unrelated costs in health economic evaluation, and GEEs could be used to estimate these costs for other diseases. Finally, these results can be used to inform policy on the expected budget impact of preventing pneumococcal infections.     

Application of Bayesian analyses to doubly randomized delayed start,matched control designs to demonstrate disease modification

Disease modification is a primary therapeutic aim when developing treatments for most chronic progressive diseases. The best treatments do not simply affect disease symptoms but fundamentally improve disease course by slowing, halting, or reversing disease progression. One of many challenges for establishing disease modification relates to the identification of adequate analytic tools to show differences in a disease course following intervention. Traditional approaches rely on the comparisons of slopes or noninferiority margins. However, it has proven difficult to conclusively demonstrate disease modification using such approaches. To address these challenges, we propose a novel adaptation of the delayed start study design that incorporates posterior probabilities identified by hierarchical Bayesian inference approaches to establish evidence for disease modification. Our models compare the size of treatment differences at the end of the delayed start period with those at the end of the early start period. Simulations that compare several models are provided. These include general linear models, repeated measures models, spline models, and model averaging. Our work supports the superiority of model averaging for accurately characterizing complex data that arise in real world applications. This novel approach has been applied to the design of an ongoing, doubly randomized, matched control study that aims to show disease modification in young persons with schizophrenia (the Disease Recovery Evaluation and Modification (DREaM) study). The application of this Bayesian methodology to the DREaM study highlights the value of this approach and demonstrates many practical challenges that must be addressed when implementing this methodology in a real world trial.     

Increasing minority employment: Are you ready to recruit?

To increase employment from desired race or gender groups, employers nearly always first turn to recruiting from outside their organization. But a few years after such initiatives are undertaken, diversity numbers typically remain low or even decrease, turnover among recruits from the sought‐after groups is high, and the efforts are threatened by their recurrent cost. Employers need to break this fruitless cycle by thinking more strategically. Without an inclusive organizational climate that retains and fully utilizes minority employees after hire, simply recruiting more such employees will not lead to sustainable changes in workforce demographics. Drawing on empirical research, this paper describes six “red flags” that identify workplaces not ready to recruit. Only after organizational changes address the deficiencies identified by the red flags will the time for minority recruitment be at hand. But by then special focused recruitment may not be necessary; when employers change their workplace cultures to become truly inclusive, word gets around.