The Effects of Childhood Sexual Abuse and Other Trauma on Drug Court Participants

Within the context of a larger study of drug court participants, this study examined the impact of traumatic experiences on psychiatric distress and on court outcomes. In the analyses, the participants (n = 229) were separated into 3 groups: childhood sexual abuse (CSA; n = 18), other trauma (n = 134), and no trauma (n = 77). The CSA group had higher mean scores on depression, anxiety, panic disorder, social phobia, somatization, and posttraumatic stress disorder than the other trauma group. Path analyses suggest that a history of trauma is a positive predictor of psychiatric distress and negative court events (positive urine screens, sanctions, etc.), with indirect effects on substance abuse severity, and failure in the drug court. These results suggest a need for the initial assessment procedure in drug courts to include a screening for trauma history, including CSA. They also suggest a need for trauma-informed care within drug courts.     

Group Perspective Formation and Strategies of Identity in a Post-threat Situation

This paper is a microsociological study of a group of people who have undergone a threat experience. Within the combined theoretical frameworks of symbolic interactionism and phenomenology, the event is seen as a "reality shock" for its participants, involving cognitive crises in group and individual definitions of the situation. The after period is one in which procedures toward making the threat experience meaningful on both group and individual levels are instituted within group networks. These procedures on a group level involve the construction of a "real" event by the objectification of time and experience and through the emergence and attribution of post-hoc norms. Strategies toward the maintenance and continuity of identity include reconstruction of the threat situation, of other participants' behavior, of post-hoc norms, or of individual behavior, and the negotiation of identity on these various bases.     

Stepwise Multiple Testing as Formalized Data Snooping

In econometric applications, often several hypothesis tests are carried out at once. The problem then becomes how to decide which hypotheses to reject, accounting for the multitude of tests. This paper suggests a stepwise multiple testing procedure that asymptotically controls the familywise error rate. Compared to related single‐step methods, the procedure is more powerful and often will reject more false hypotheses. In addition, we advocate the use of studentization when feasible. Unlike some stepwise methods, the method implicitly captures the joint dependence structure of the test statistics, which results in increased ability to detect false hypotheses. The methodology is presented in the context of comparing several strategies to a common benchmark. However, our ideas can easily be extended to other contexts where multiple tests occur. Some simulation studies show the improvements of our methods over previous proposals. We also provide an application to a set of real data.     

Experimental Results Indicating Lattice‐Dependent Policies May Be Optimal for General Assemble‐To‐Order Systems

We consider an assemble‐to‐order (ATO) system with multiple products, multiple components which may be demanded in different quantities by different products, possible batch ordering of components, random lead times, and lost sales. We model the system as an infinite‐horizon Markov decision process under the average cost criterion. A control policy specifies when a batch of components should be produced, and whether an arriving demand for each product should be satisfied. Previous work has shown that a lattice‐dependent base‐stock and lattice‐dependent rationing (LBLR) policy is an optimal stationary policy for a special case of the ATO model presented here (the generalized M‐system). In this study, we conduct numerical experiments to evaluate the use of an LBLR policy for our general ATO model as a heuristic, comparing it to two other heuristics from the literature: a state‐dependent base‐stock and state‐dependent rationing (SBSR) policy, and a fixed base‐stock and fixed rationing (FBFR) policy. Remarkably, LBLR yields the globally optimal cost in each of more than 22,500 instances of the general problem, outperforming SBSR and FBFR with respect to both objective value (by up to 2.6% and 4.8%, respectively) and computation time (by up to three orders and one order of magnitude, respectively) in 350 of these instances (those on which we compare the heuristics). LBLR and SBSR perform significantly better than FBFR when replenishment batch sizes imperfectly match the component requirements of the most valuable or most highly demanded product. In addition, LBLR substantially outperforms SBSR if it is crucial to hold a significant amount of inventory that must be rationed.