Young Fathers: An Analysis of Risk Behaviors and Service Needs

One hundred and twenty eight young fathers participated in an assessment of risk behaviors and service needs prior to entering a program for young fathers. Of this group, 73% were unemployed, 69% were school drop-outs, almost 40% had substance abuse problems, close to 30% had committed a felony, and less than half had declared paternity for their children. The majority of these young fathers desired employment services and educational/vocational training. Despite their risk behaviors, young fathers did not want substance abuse counseling, child support services, or help in obtaining a GED. There appears to be a discrepancy between the problems and needs, and the services which these young fathers requested. Many young fathers believed employment would be a panacea to all their problems. These findings suggest that more attention should be given to examining these issues in programs targeting young fathers. Services such as preventive health and mental health should be considered prior to the final goals of employment and establishment of paternity.     

A novel equivalence probability weighted power prior for using historical control data in an adaptive clinical trial design: A comparison to standard methods

A standard two-arm randomised controlled trial usually compares an intervention to a control treatment with equal numbers of patients randomised to each treatment arm and only data from within the current trial are used to assess the treatment effect. Historical data are used when designing new trials and have recently been considered for use in the analysis when the required number of patients under a standard trial design cannot be achieved. Incorporating historical control data could lead to more efficient trials, reducing the number of controls required in the current study when the historical and current control data agree. However, when the data are inconsistent, there is potential for biased treatment effect estimates, inflated type I error and reduced power. We introduce two novel approaches for binary data which discount historical data based on the agreement with the current trial controls, an equivalence approach and an approach based on tail area probabilities. An adaptive design is used where the allocation ratio is adapted at the interim analysis, randomising fewer patients to control when there is agreement. The historical data are down-weighted in the analysis using the power prior approach with a fixed power. We compare operating characteristics of the proposed design to historical data methods in the literature: the modified power prior; commensurate prior; and robust mixture prior. The equivalence probability weight approach is intuitive and the operating characteristics can be calculated exactly. Furthermore, the equivalence bounds can be chosen to control the maximum possible inflation in type I error.     

Mother-Child Relations and Adolescent Sexual Attitudes and Behavior

We hypothesize that children who are close to their parents are more likely to have attitudes and behavior that are consistent with their parents' values than children who are not close to their parents. Using data from a probability sample of 888 mother-child pairs of white women in the Detroit metropolitan area, we tested this hypothesis, using both mother's and child's attitudes toward premarital sexual intercourse and the child's report of whether he or she had engaged in premarital sexual intercourse. Unlike most previous work in this area, which has posited a direct association between the closeness of the mother-child relation and the child's attitudes and behavior, we found that the quality of the relation interacted with the mother's attitudes in its effects on the child's attitudes and behavior. Children with close relations with their mothers were more likely to hold attitudes and behave in a manner consistent with their mothers' own attitudes than children with more distant relations.     

A semiparametric nonlinear mixed model approach to phase I profile monitoring

When process data follow a particular curve in quality control, profile monitoring is suitable and appropriate for assessing process stability. Previous research in profile monitoring focusing on nonlinear parametric (P) modeling, involving both fixed and random-effects, was made under the assumption of an accurate nonlinear model specification. Lately, nonparametric (NP) methods have been used in the profile monitoring context in the absence of an obvious linear P model. This study introduces a novel technique in profile monitoring for any nonlinear and auto-correlated data. Referred to as the nonlinear mixed robust profile monitoring (NMRPM) method, it proposes a semiparametric (SP) approach that combines nonlinear P and NP profile fits for scenarios in which a nonlinear P model is adequate over part of the data but inadequate of the rest. These three methods (P, NP, and NMRPM) account for the auto-correlation within profiles and treats the collection of profiles as a random sample with a common population. During Phase I analysis, a version of Hotelling’s T² statistic is proposed for each approach to identify abnormal profiles based on the estimated random effects and obtain the corresponding control limits. The performance of the NMRPM method is then evaluated using a real data set. Results reveal that the NMRPM method is robust to model misspecification and performs adequately against a correctly specified nonlinear P model. Control charts with the NMRPM method have excellent capability of detecting changes in Phase I data with control limits that are easily computable.     

Exploring Politicized Trust in a Racially Diverse Computer Science Classroom

ABSTRACT

Over the past decade, abroad range of policymakers, corporations, educators, and scholars in the United States have catapulted computer science (CS) education from the sidelines to the center of K-12 public education discourse. While calls for CS education are often framed in terms of national and economic competitiveness, there is a growing interest amongst equity scholars in curricular interventions that directly engage the ethical and sociopolitical issues surrounding CS and its role in society. Yet, less attention has been given to how sociocultural dynamics of classrooms play a mediating role in these spaces. Drawing on video data from an equity-oriented CS classroom, we argue aconflict that arose during design activities was rooted in alack of trust and solidarity between students. Ultimately, we make the case that in addition to curricular innovations, equity efforts in CS education must prioritize the cultivation of positive student relationships.     

The Best Predictors of Survival: Do They Vary by Age,Sex, and Race?

We consider a broad set of variables used by social scientists and clinicians to identify the leading predictors of five‐year survival among American adults. We address a question not considered in earlier research: Do the strongest predictors of survival vary by age, sex or race/ethnicity? The analysis uses hazard models with 30 well‐established predictors to examine five‐year survival in the National Health and Nutrition Examination Survey. We find that the simple measure of self‐assessed health and self‐reported measures of functional ability and disability are the strongest predictors in all demographic groups, and are generally ranked considerably higher than biomarkers. Among the biomarkers, serum albumin is highly ranked in most demographic groups, whereas clinical measures of cardiovascular and metabolic function are consistently among the weakest predictors. Despite these similarities, there is substantial variation in the leading predictors across demographic groups, most notably by race and ethnicity     

Beyond Self‐Reports: Changes in Biomarkers as Predictors of Mortality

The proliferation of biosocial surveys has increased the importance of weighing the costs and benefits of adding biomarker collection to population‐based surveys. A crucial question is whether biomarkers offer incremental value beyond self‐reported measures, which are easier to collect and impose less respondent burden. We use longitudinal data from a nationally representative sample of older Taiwanese (aged 54+ in 2000, examined in 2000 and 2006 with mortality follow‐up through 2011) to address that question with respect to predicting all‐cause mortality. A summary measure of biomarkers improves mortality prediction (as measured by the area under the receiver operating characteristic curve) compared with self‐reports alone, but individual biomarkers perform better than the summary score. We find that incorporating change in biomarkers over a six‐year period yields a small improvement in mortality prediction compared with one‐time measurement. But, is the incremental value worth the costs?     

Local realities: a frontier perspective on child protection team practice

This article presents a case study of a child protection team in a frontier county in Montana. Narratives from individual and group interviews highlight team members' experiences and the structuring of team practice in their frontier community, the power and challenge of community culture and history, and the interplay of constructions of children and child abuse as key elements that influence and shape team practice. This exploration suggests a framework for rethinking child protection team practice in diverse locations.     

Understanding the Juggling Act: Gendered Preferences and Social Structural Constraints

In this paper we use longitudinal data to test the strength of individual preferences and structural variables as explanations for married women's labor force participation. Data drawn from a subset of the Career Development Study are used to compare gendered preferences measured toward the end of adolescence vs. work and family structural variables as predictors of the actual number of hours married women work for pay. Family structures that push women out of the labor force and pull them into family work prove to be the strongest predictor of married women's employment hours, with work structures (e.g., aspects of good jobs) and the subjective definition of paid work as a career also being substantively important for explaining hours in the labor force. Our findings also indicate that attitudes formed before and during early adolescence do have a weak but statistically significant effect on married women's labor force participation, at least for baby boom women.     

Violence and the costs of caring for a family member with severe mental illness

Drawing on the stress paradigm and using data from the Duke Mental Health Study, this paper investigates the links between violence by and against persons with severe mental illness and their caregivers' financial burden (e.g., number of financial contributions and perceived financial strain). In addition to violence, substance use and medication noncompliance are included in a series of multivariate regression models predicting caregiver financial burden that include measures for disruptive behaviors, personal needs, social supports, and caregiver characteristics. The findings show that violent perpetration and violent victimization increase the number of financial contributions and perceived financial strain. Nonviolent victimization, in contrast, decreases caregiver financial burden. Number of financial contributions and perceived financial strain are greatest when the family member is violent and extremely disruptive or needs assistance with personal care. Neither substance use nor medication noncompliance contributed to caregiver financial burden. Co-residence, female sex, parenthood, and instrumental social support were also associated with financial burden.