Transgenerational transmission of trauma in families of Holocaust survivors: The consequences of extreme family functioning on resilience, Sense of Coherence, anxiety and depression.

BACKGROUND: The psychological transmission of the noxious effects of a major trauma from one generation to the next remains unclear. The present study aims to identify possible mechanisms explaining this transmission among families of Holocaust Survivors (HS). We hypothesized that the high level of depressive and anxiety disorders (DAD) among HS impairs family systems, which results in damaging coping strategies of their children (CHS) yielding a higher level of DAD. METHODS: 49 CHS completed the Resilience Scale for Adults, the Hopkins Symptom Check List-25, the 13-Item Sense of Coherence (SOC) scale, and the Family Adaptability and Cohesion Scale. We test a mediation model with Family types as the predictor; coping strategies (i.e. Resilience or SOC) as the mediator; and DAD as the outcome variable. RESULTS: Results confirm that the CHS׳ family types are more often damaged than in general population. Moreover, growing in a damaged family seems to impede development of coping strategies and, therefore, enhances the occurrence of DAD. LIMITATIONS: The present investigation is correlational and should be confirmed by other prospective investigations. CONCLUSIONS: At a theoretical level we propose a mechanism of transmission of the noxious effects of a major trauma from one generation to the next through family structure and coping strategies. At a clinical level, our results suggest to investigate the occurrence of trauma among parents of patients consulting for DAD and to reinforce their coping strategies.

Local and global error models to improve uncertainty quantification

In groundwater applications, Monte Carlo methods are employed to model the uncertainty on geological parameters. However, their brute-force application becomes computationally prohibitive for highly detailed geological descriptions, complex physical processes, and a large number of realizations. The Distance Kernel Method (DKM) overcomes this issue by clustering the realizations in a multidimensional space based on the flow responses obtained by means of an approximate (computationally cheaper) model; then, the uncertainty is estimated from the exact responses that are computed only for one representative realization per cluster (the medoid). Usually, DKM is employed to decrease the size of the sample of realizations that are considered to estimate the uncertainty. We propose to use the information from the approximate responses for uncertainty quantification. The subset of exact solutions provided by DKM is then employed to construct an error model and correct the potential bias of the approximate model. Two error models are devised that both employ the difference between approximate and exact medoid solutions, but differ in the way medoid errors are interpolated to correct the whole set of realizations. The Local Error Model rests upon the clustering defined by DKM and can be seen as a natural way to account for intra-cluster variability; the Global Error Model employs a linear interpolation of all medoid errors regardless of the cluster to which the single realization belongs. These error models are evaluated for an idealized pollution problem in which the uncertainty of the breakthrough curve needs to be estimated. For this numerical test case, we demonstrate that the error models improve the uncertainty quantification provided by the DKM algorithm and are effective in correcting the bias of the estimate computed solely from the MsFV results. The framework presented here is not specific to the methods considered and can be applied to other combinations of approximate models and techniques to select a subset of realizations