Predictors of Intrusive Sexual Behaviors in Preschool-Aged Children

Intrusive sexual behaviors (ISBs) are a specific type of problematic sexual behavior characterized by the invasive nature of the acts (e.g., touching others’ private parts, attempting intercourse; Friedrich, 1997). The limited amount of research on ISBs has focused on sexual abuse history as the primary predictor. However, Friedrich, Davies, Feher, and Wright (2003) found that ISBs in children up to age 12 were related to four broad conceptual factors: (a) exposure to sexual content, (b) exposure to violent behavior, (c) family adversity, and (d) child vulnerabilities. The current study sought to replicate Friedrich’s study using a clinical sample of 217 preschool-aged children (ages two to six). Results supported variables from within the child vulnerabilities construct (externalizing behaviors, β_EXT = 0.032, p = 0.001), post-traumatic stress disorder (PTSD) criteria met (β_PTSD = 0.177, p = 0.02), and an inverse relationship with age (β_AGE = ?0.206, p = 0.024). These results highlight the importance of considering childhood behavioral patterns and reactivity to traumatic events as correlates of ISBs in young children.     

Comments on Willis,Wells and Picou

Most research relating objective indicators of the quality of environment to subjective indicators of the quality of life has failed to find a relationship between the two. This research, by using community data, establishes an association between the local environment and perceived life quality, and in doing so, provides empirical support for focusing on local as welt as national investigations of life quality.     

An Evaluation of Behavioral Health Compliance and Microbial Risk Factors on Student Populations Within a High-Density Campus

Abstract

Objective: The aim of this Canadian study was to assess student behavioral response to disease transmission risk, while identifying high microbial deposition/transmission sites. Participants: A student survey was conducted during October 2009. Methods: The methods included a survey of students to assess use of health services, vaccination compliance, and hygiene along with a microbial analysis of potential transmission sites targeting specific residence buildings on campus. Results: Results indicated that most students maintained that they were worried about H1N1 and reported making changes in hygienic behavior, with the majority not planning to be vaccinated. The microbial analysis indicated contamination of fomites in co-ed residences to be higher than either male or female student residences. Conclusions: A consideration of physical space along with behavioral factors is required in order to properly assess risk pathways in the establishment of an evidence-based infection control plan for universities and their contiguous communities.     

Depression,anxiety, and tobacco use: Overlapping impediments to sleep in a national sample of college students

ABSTRACT

Objectives: To examine how tobacco use and depression/anxiety disorders are related to disturbed sleep in college students. Participants: 85,138 undergraduate respondents (66.3% female, 74.5% white, non-Hispanic, ages 18–25) from the Spring 2011 American College Health Association–National College Health Assessment II database. Methods: Multivariate analyses of tobacco use (none, intermediate, daily) and mental health (diagnosed and/or symptomatic depression or anxiety) were used to predict sleep disturbance. Results: Daily tobacco use was associated with more sleep problems than binge drinking, illegal drug use, obesity, gender, and working >20 hours/week. Students with depression or anxiety reported more sleep disturbances than individuals without either disorder, and tobacco use in this population was associated with the most sleep problems. Conclusions: Tobacco use and depression/anxiety disorders are both independently associated with more sleep problems in college students. Students with depression and/or anxiety are more likely to be daily tobacco users, which likely exacerbates their sleep problems.     

When Working Hard Is Not Enough for Female and Racial/Ethnic Minority Apprentices in the Highway Trades

Drawing on employment records, qualitative interviews, and a survey, we explore the experiences of apprentices in the highway trades in Oregon. We demonstrate that female and racial/ethnic minority apprentices have lower rates of recruitment and retention and disproportionately face challenges with interpersonal interactions, hiring practices, and supervisory practices. Yet, we find a pervasive narrative that attributes apprentices' success to “hard work,” which contributes to the legitimacy of these inequalities. Consistent with the conceptualization of work organizations as inequality regimes, we argue that the apprenticeship system has policies, practices, and ideologies that are on the surface gender and race/ethnicity neutral, yet lead to the perpetuation of inequalities.     

Shrinkage confidence intervals for the normal mean: Using a guess for greater efficiency

If the unknown mean of a univariate population is sufficiently close to the value of an initial guess then an appropriate shrinkage estimator has smaller average squared error than the sample mean. This principle has been known for some time, but it does not appear to have found extension to problems of interval estimation. The author presents valid two‐sided 95% and 99% “shrinkage” confidence intervals for the mean of a normal distribution. These intervals are narrower than the usual interval based on the Student distribution when the population mean lies in such an “effective interval.” A reduction of 20% in the mean width of the interval is possible when the population mean is sufficiently close to the value of the guess. The author also describes a modification to existing shrinkage point estimators of the general univariate mean that enables the effective interval to be enlarged.     

A mixture model for random graphs

The Erdös–Rényi model of a network is simple and possesses many explicit expressions for average and asymptotic properties, but it does not fit well to real-world networks. The vertices of those networks are often structured in unknown classes (functionally related proteins or social communities) with different connectivity properties. The stochastic block structures model was proposed for this purpose in the context of social sciences, using a Bayesian approach. We consider the same model in a frequentest statistical framework. We give the degree distribution and the clustering coefficient associated with this model, a variational method to estimate its parameters and a model selection criterion to select the number of classes. This estimation procedure allows us to deal with large networks containing thousands of vertices. The method is used to uncover the modular structure of a network of enzymatic reactions.