Predictors of forensic CBT group attendance and program completion

With the increased focus on rehabilitation, county probation agencies may contract with community-based programs to provide treatment services. Forensic Cognitive Behavioral Therapy (F-CBT) is often used in group work to address probation client risk factors and prevent recidivism. This study examined demographic and risk factors of 534 adult probation clients referred to an F-CBT program to determine predictors of program intake, participation, and completion. Results showed that probation type, higher risk neighborhood of residence, and recidivism risk score were predictors of intake completion, but only recidivism risk scores predicted program completion. Implications and methods to increase program engagement are discussed.     

Assessing the validity of a single-item HIV risk stage-of-change measure

This study examined the validity of a single-item measure of HIV risk stage of change that HIV prevention contractors were required to collect by the California State Office of AIDS. The single-item measure was compared to the more conventional University of Rhode Island Change Assessment (URICA). Participants were members of Los Angeles County-defined behavioral risk groups recruited from a mobile HIV testing program (N = 123). The study found low correspondence between participants' stage of change on the single-item and the URICA, suggesting that they do not assess the same construct, and that the single item may not provide a valid measure. The current data suggests that the single-item measure should be revised or abandoned in favor of more conventional stage-of-change measures.     

Increasing walking in college students using a pedometer intervention: differences according to body mass index

Objective: The researchers assessed the effectiveness of a pedometer intervention and differences in walking behaviors according to body mass index (BMI). Participants: Two hundred ninety college students completed the intervention from January to February 2005. Methods: Participants wore pedometers 5 days per week for 12 weeks and completed questionnaires assessing demographic information. The authors calculated daily step averages for weeks 1, 6, and 12. They then classified students as underweight (UW), normal weight (NW), or overweight/obese, by BMI. The authors analyzed data using repeated-measures analysis of variance. Results: The average number of daily steps increased from week 1 to week 6 (p < .001) and week 12 (p = .002). UW participants reported the fewest steps at each time point, but the difference was significant only when compared with NW participants (p = .03). Conclusions: These results support the effectiveness of a pedometer intervention to increase walking in college students. Health benefits other than weight management should be emphasized to maximize the effects for all students.     

Leaving the parental home in Australia over the generations: Evidence from the Household,Income and Labour Dynamics in Australia (Hilda) Survey

The decision to remain in or leave the parental home represents the first housing career choice of young people. In this paper, we examine the parental home leaving outcomes for Australian birth cohorts in the twentieth century using recall questions contained in the Household, Income and Labour Dynamics in Australia Survey. The findings from survival analysis and hazard function modelling is that a continuous, gradual reduction in the age of first leaving the parental home in Australia is evident up to the 1947–51 birth cohort, followed by stability and then a rise in age of first leaving home for the most recent birth cohorts. Birth cohort effects remain significant even after other measured determinants of parental home leaving are taken into account. The paper confirms the strong roles that education, family background and ethnicity have on parental home leaving outcomes.     

THE POLITICAL ECONOMY OF REGULATION IN MARKETS WITH NAÏVE CONSUMERS

In a model of a competitive industry selling base goods and add‐ons, we investigate the conditions under which citizen‐consumers will support policies that eliminate behavioral inefficiencies induced by naïve consumers. Unregulated competitive markets have two effects: they produce deadweight losses, and they redistribute income away from biased consumers. Both unbiased and naïve consumers believe that they benefit from this redistribution (the naïve consumers are wrong), so support for efficiency‐improving regulation is limited. Extending our model to consumers with partial sophistication about their naïveté, we predict patterns of regulation consistent with the form and timing of the Credit Card Accountability Responsibility and Disclosure (CARD) Act of 2009.     

Managing the Peace: Exorcising Colonial Ghosts

Public Organization Review -     

On Causal Inferences for Personalized Medicine: How Hidden Causal Assumptions Led to Erroneous Causal Claims About the D-Value

Abstract

Personalized medicine asks if a new treatment will help a particular patient, rather than if it improves the average response in a population. Without a causal model to distinguish these questions, interpretational mistakes arise. These mistakes are seen in an article by Demidenko that recommends the “D-value,” which is the probability that a randomly chosen person from the new-treatment group has a higher value for the outcome than a randomly chosen person from the control-treatment group. The abstract states “The D-value has a clear interpretation as the proportion of patients who get worse after the treatment” with similar assertions appearing later. We show these statements are incorrect because they require assumptions about the potential outcomes which are neither testable in randomized experiments nor plausible in general. The D-value will not equal the proportion of patients who get worse after treatment if (as expected) those outcomes are correlated. Independence of potential outcomes is unrealistic and eliminates any personalized treatment effects; with dependence, the D-value can even imply treatment is better than control even though most patients are harmed by the treatment. Thus, D-values are misleading for personalized medicine. To prevent misunderstandings, we advise incorporating causal models into basic statistics education.