Client narratives about experiences with a multicouple treatment program for intimate partner violence

A handful of clinical trials have concluded that conjoint couples treatment for intimate partner violence is safe and at least as effective as conventional batterer intervention programs, yet very few researchers have explored couples' perspectives on conjoint treatment. Using qualitative narrative analysis methodology, the researchers conducted 48 client participant interviews and five staff interviews to better understand couples' experiences of a conjoint treatment program for intimate partner violence. Themes that recurred throughout participants' narratives included (a) perceptions of safety, (b) benefits received, (c) attitudinal change, (d) behavioral change, and (e) important learning. Research directions and training implications are discussed.     

Understanding "elder abuse and neglect": a critique of assumptions underpinning responses to the mistreatment and neglect of older people

This article provides an overview of the ways in which the mistreatment and neglect of older people have come to be understood as a social problem, one which is underpinned by a variety of substantive and theoretical assumptions. It connects the process of conceptualizing elder abuse and neglect to political-economic and social evolution. The authors draw on a review of the literature, government sources, interest group websites, and their own research to provide a critical commentary illustrating how these understandings have become manifest in legislation, policies, and programs pertaining to "elder abuse and neglect" in Canada. Suggestions are provided for changes in direction for policies, programs, and research.     

Male Batterers’ Alcohol Use and Gambling Behavior

Little work has examined the interrelations among intimate partner violence (IPV), alcohol use, and gambling behavior, and no studies have examined these relationships among males court-ordered to batterer intervention programs (BIPs). The aim of the current investigation was to explore the associations between IPV, alcohol use, and gambling behavior among 341 males court-mandated to attend BIPs utilizing self-report measures. Voluntary, anonymous questionnaires were administered and completed during regularly scheduled BIP sessions. Compared to the general population, a higher percentage of the sample met criteria for pathological gambling (9%), and problem gambling (17%). Further, males exhibiting pathological gambling were more likely to be hazardous drinkers, and hazardous drinkers were more likely to exhibit pathological gambling. Additionally, pathological gamblers were at an increased risk for the perpetration of both physical and sexual aggression. Finally, gambling behavior uniquely predicted the perpetration of sexual aggression above and beyond alcohol use, impulsivity, and relationship satisfaction. The implications of these results for future research and intervention are discussed.     

Neighborhood Concentrated Disadvantage and Adult Mortality: Insights for Racial and Ethnic Differences

While racial and ethnic differences in mortality are pervasive and well documented, less is known about how mortality risk varies by neighborhood socioeconomic status across racial and ethnic identity. We conducted a prospective analysis on a sample of adults living at or below 300% poverty with 8 years of the National Health Interview Survey (N = 159,400) linked to 11,600 deaths to examine the association between neighborhood disadvantage and mortality for non-Hispanic whites, non-Hispanic blacks, and U.S.- and foreign-born Hispanics. Using multilevel logistic regression, we find that the probability of death from any cause for lower-income adults is higher in more-disadvantaged neighborhoods, compared to less-disadvantaged neighborhoods, but only for whites. The adjusted likelihood of death for blacks and foreign-born Hispanics is not associated with neighborhood disadvantage, and the likelihood of death for U.S.-born Hispanics is lower in more-disadvantaged neighborhoods. While future research and policy should focus on improving health-promoting resources in all communities, care should be given to better understanding why race/ethnic groups have differential mortality returns with respect to area-specific socioeconomic conditions.     

The role of population size in the determination and prediction of population forecast errors: An evaluation using conifidence intervals for subcounty areas

Producers of population forecasts acknowledge the uncertainty inherent in trying to predict the future and should warn about the likely error of their forecasts. Confidence intervals represent one way of quantifying population forecast error. Most of the work in this area relates to national forecasts; although, confidence intervals have been developed for state and county forecasts. A few studies have examined subcounty forecast error, however, they only measured point estimates of error. This paper describes a technique for making subcounty population forecasts and for generating confidence intervals around their forecast error. It also develops statistical equations for calculating point estimates and confidence intervals for areas with different population sizes. A non-linear, inverse relationship between population size and forecast accuracy was found and we demonstrate the ability to accurately predict average forecast error and confidence intervals based on this relationship.     

Single Session Thinking 2020

This update presents key developments since Talmon’s original (1990) publication, including an account of the three international symposia which have taken place since the last ANZJFT special edition on single session therapy in 2012, and the major compilations that followed the symposia. Underlying elements that unite different single session approaches are explored, and an attempt is made to provide a terminology that is inclusive and coherent. As reflected in the title, the term ‘single session thinking’ is suggested as an over-arching term for the approach that is no longer limited to the therapy room, but reaches into many different contexts. In conclusion, possible future clinical and research developments in the field of single session thinking and practice are reflected upon, and the implications for contemporary health care delivery considered.     

Intergenerational Childhood Maltreatment in Persons with DSM-IV Pathological Gambling and Their First-Degree Relatives

This study investigates the characteristics of individuals with DSM-IV pathological gambling (PG) who experienced childhood maltreatment and rates of maltreatment occurring in their first-degree relatives (FDRs). 94 subjects with DSM-IV PG, 91 controls, and 312 FDRs were assessed for childhood maltreatment as part of a family study of PG. Maltreatment was evaluated using the Revised Childhood Experiences Questionnaire. The Family Assessment Device was used to evaluate the functionality of the PG subject’s (or control’s) family of origin. Data were analyzed using logistic regression by the method of generalized estimating equations. Rates of maltreatment were significantly higher in subjects with PG than controls (61 vs. 25 %, P < 0.001). Subjects with PG who experienced maltreatment were more likely to be female, had more severe PG symptoms, had co-occurring mood and anxiety disorders, and reported greater early family life dysfunction than those with PG who did not experience maltreatment. Rates of maltreatment were higher in FDRs of PG subjects than controls (41 vs. 24 %, P = .002). Rates in FDRs of individuals with PG who experienced maltreatment themselves were still higher that in FDRs of those with PG who did not experience maltreatment (50 vs. 28 %, P = .009). The former were also more likely to have anxiety disorders, substance use disorders, and suicide attempts. The results suggest that childhood maltreatment in persons with PG is common and intergenerational. Rates of maltreatment in FDRs of PG subjects are high, particularly among those who experienced abuse. The implications of the findings are discussed.     

Comparison of Type I error rates and statistical power of different propensity score methods

Propensity score analysis (PSA) is a technique to correct for potential confounding in observational studies. Covariate adjustment, matching, stratification, and inverse weighting are the four most commonly used methods involving propensity scores. The main goal of this research is to determine which PSA method performs the best in terms of protecting against spurious association detection, as measured by Type I error rate, while maintaining sufficient power to detect a true association, if one exists. An examination of these PSA methods along with ordinary least squares regression was conducted under two cases: correct PSA model specification and incorrect PSA model specification. PSA covariate adjustment and PSA matching maintain the nominal Type I error rate, when the PSA model is correctly specified, but only PSA covariate adjustment achieves adequate power levels. Other methods produced conservative Type I Errors in some scenarios, while liberal Type I error rates were observed in other scenarios.     

Binary Time Series Modeling with Application to Adhesion Frequency Experiments

Repeated adhesion frequency assay is the only published method for measuring the kinetic rates of cell adhesion. Cell adhesion plays an important role in many physiological and pathological processes. Traditional analysis of adhesion frequency experiments assumes that the adhesion test cycles are independent Bernoulli trials. This assumption can often be violated in practice. Motivated by the analysis of repeated adhesion tests, a binary time series model incorporating random effects is developed in this paper. A goodness-of-fit statistic is introduced to assess the adequacy of distribution assumptions on the dependent binary data with random effects. The asymptotic distribution of the goodness-of-fit statistic is derived and its finite-sample performance is examined via a simulation study. Application of the proposed methodology to real data from a T-cell experiment reveals some interesting information, including the dependency between repeated adhesion tests.     

Sampling schemes for generalized linear Dirichlet process random effects models

We evaluate MCMC sampling schemes for a variety of link functions in generalized linear models with Dirichlet process random effects. First, we find that there is a large amount of variability in the performance of MCMC algorithms, with the slice sampler typically being less desirable than either a Kolmogorov–Smirnov mixture representation or a Metropolis–Hastings algorithm. Second, in fitting the Dirichlet process, dealing with the precision parameter has troubled model specifications in the past. Here we find that incorporating this parameter into the MCMC sampling scheme is not only computationally feasible, but also results in a more robust set of estimates, in that they are marginalized-over rather than conditioned-upon. Applications are provided with social science problems in areas where the data can be difficult to model, and we find that the nonparametric nature of the Dirichlet process priors for the random effects leads to improved analyses with more reasonable inferences.