Changes in couple,parenting, and individual functioning following Family Expectations program participation

The current study investigated changes in couple, parenting, and individual functioning following participation in Family Expectations, a relationship and parenting education program for new or expectant parents. The sample comprised 339 couples who participated in most sessions of the Family Expectations program and completed assessments at three different time points over a 12-month period. Study analyses examined: (a) change shortly following completion of the program, (b) associations between short-term change and subsequent change in outcomes at 12-month follow-up, and (c) differences in short-term change between married and unmarried couples. Significant improvements were observed in all three domains at short-term follow-up. Short-term changes, particularly for psychological distress, were predictive of long-term change in multiple domains. Few moderation effects by marital status were evident; those that appeared suggested stronger effects for married participants compared to unmarried. Study findings inform ongoing discussions into the utility of federally-funded relationship and parenting programming.     

Subjective evaluations of alcohol-related consequences among college students: Experience with consequences matters

Objective: Research suggests college students rate some alcohol-related consequences less negatively than others, yet it is unclear how or when these differences in perception develop. The current study compared college students' subjective evaluations of alcohol-related consequences that they had and had not experienced in order to test the hypothesis that students become desensitized to the consequences they experience. Participants: Participants were 269 undergraduate students enrolled at a large, public, Midwestern university. Methods: Participants completed measures of drinking behaviors, consequences experienced, and subjective evaluations of consequences via an online survey. Results: Participants rated the consequences they had personally experienced more positively than those they had not experienced. Similarly, individuals who reported experiencing consequences rated them as significantly more positive than those who had not experienced the same consequences. Conclusions: Experience with consequences is associated with more positive evaluations of those consequences. Therefore, it may be important to consider individuals' experiences with, and evaluations of, alcohol-related consequences in college student drinking interventions.     

The Development and Implementation of a Multi‐Couple Therapy Model with Torture Survivors in the Democratic Republic of the Congo

In this article, we present development and feasibility of implementation of a multi‐couple group for use with torture‐surviving couples. The model was developed in the Democratic Republic of the Congo in a community that experienced widespread torture during the wars from 1998 to 2004. The Torture‐Surviving Couple Group model is a short‐term intervention designed to use few human resources to address relational difficulties resulting from exposure to traumatic stressors. The model was guided by critical and feminist epistemologies and employed an ecological lens to incorporate neurobiology and attachment processes along with narrative therapy techniques. An existing multi‐couple group model for addressing violence (Stith, Rosen, McCollum, & Thomsen, 2004 ), and a stage model for healing trauma (Herman, 1992 ) also informed the structural development of the Torture‐Surviving Couple Group model. Couple groups were conducted using a 10‐session program with 13 couples who met weekly. Session themes were incorporated into four phases emphasizing: (a) preparation; (b) safety and stabilization; (c) processing the relationship effects of trauma and grief; and (d) reintegration and rebuilding couple and family life. Couples reported and showed remarkable progress in their relationships after participating in the groups. Clinical and research implications and discussed along with the feasibility of developing and testing the model in a post‐conflict low income setting.     

Bayesian estimation of hispanic fertility hazards from survey and population data*

Previous studies have demonstrated both large gains in efficiency and reductions in bias by incorporating population information in regression estimation with sample survey data. These studies, however, assumed that the population values are exact. This assumption is relaxed here through a Bayesian extension of constrained maximum likelihood estimation applied to U.S. Hispanic fertility. The Bayesian approach allows for the use of both auxiliary survey data and expert judgment in making adjustments to published Hispanic Population fertility rates, and for the estimation of uncertainty about these adjustments. Compared with estimation from sample survey data only, the Bayesian constrained estimator results in much greater precision in the age pattern of the baseline fertility hazard and therefore of the predicted values for any given combination of socioeconomic variables. The use of population data in combination with survey data may therefore be highly advantageous even when the population data are known to have significant levels of nonsampling error.     

ESTIMATING WITHIN-HOUSEHOLD CONTACT NETWORKS FROM EGOCENTRIC DATA

Acute respiratory diseases are transmitted over networks of social contacts. Large-scale simulation models are used to predict epidemic dynamics and evaluate the impact of various interventions, but the contact behavior in these models is based on simplistic and strong assumptions which are not informed by survey data. These assumptions are also used for estimating transmission measures such as the basic reproductive number and secondary attack rates. Development of methodology to infer contact networks from survey data could improve these models and estimation methods. We contribute to this area by developing a model of within-household social contacts and using it to analyze the Belgian POLYMOD data set, which contains detailed diaries of social contacts in a 24-hour period. We model dependency in contact behavior through a latent variable indicating which household members are at home. We estimate age-specific probabilities of being at home and age-specific probabilities of contact conditional on two members being at home. Our results differ from the standard random mixing assumption. In addition, we find that the probability that all members contact each other on a given day is fairly low: 0.49 for households with two 0-5 year olds and two 19-35 year olds, and 0.36 for households with two 12-18 year olds and two 36+ year olds. We find higher contact rates in households with 2-3 members, helping explain the higher influenza secondary attack rates found in households of this size.     

Generalised Linear Models Incorporating Population Level Information: An Empirical Likelihood Based Approach

In many situations information from a sample of individuals can be supplemented by population level information on the relationship between a dependent variable and explanatory variables. Inclusion of the population level information can reduce bias and increase the efficiency of the parameter estimates.Population level information can be incorporated via constraints on functions of the model parameters. In general the constraints are nonlinear making the task of maximum likelihood estimation harder. In this paper we develop an alternative approach exploiting the notion of an empirical likelihood. It is shown that within the framework of generalised linear models, the population level information corresponds to linear constraints, which are comparatively easy to handle. We provide a two-step algorithm that produces parameter estimates using only unconstrained estimation. We also provide computable expressions for the standard errors. We give an application to demographic hazard modelling by combining panel survey data with birth registration data to estimate annual birth probabilities by parity.     

Adjusting for Network Size and Composition Effects in Exponential-Family Random Graph Models

Exponential-family random graph models (ERGMs) provide a principled way to model and simulate features common in human social networks, such as propensities for homophily and friend-of-a-friend triad closure. We show that, without adjustment, ERGMs preserve density as network size increases. Density invariance is often not appropriate for social networks. We suggest a simple modification based on an offset which instead preserves the mean degree and accommodates changes in network composition asymptotically. We demonstrate that this approach allows ERGMs to be applied to the important situation of egocentrically sampled data. We analyze data from the National Health and Social Life Survey (NHSLS).     

Peer Educators Reach College Students with Nutrition Information

Abstract

Leighton C. Whitaker and Jeffrey W. Pollard, eds. Campus Violence: Kinds, Causes, and Cures. New York: Haworth Press, 1993. 313 pps, $39.95.