Mothers’ Experience of Post-Traumatic Growth in Pediatric Kidney Transplantation

ABSTRACT

Parenting a child with chronic kidney disease has a profound impact on the parental caregivers across social, emotional, and physical functioning. As the survival rates for children with chronic kidney disease increase, the demands on parents caring for these children intensify. The aim of this study was to understand the lived experience of being a mother of a child who has undergone kidney transplantation. Seven mothers caring for children with chronic kidney disease in Ontario, Canada participated in in-depth interviews that were analyzed according to the principles of hermeneutic phenomenology. The present study presents the findings concerning post-traumatic growth: personal strength, new possibilities, enhanced relationships, appreciation of life and spiritual change. Recognizing positive aspects of stressful situations and the potential for growth can impact the practice of social workers and other health-care professionals. Ultimately the assistance that is provided to parent caregivers can be improved with a better understanding of the whole experience.     

Facilitating the Calculation of the Efficient Score Using Symbolic Computing

The score statistic continues to be a fundamental tool for statistical inference. In the analysis of data from high-throughput genomic assays, inference on the basis of the score usually enjoys greater stability, considerably higher computational efficiency, and lends itself more readily to the use of resampling methods than the asymptotically equivalent Wald or likelihood ratio tests. The score function often depends on a set of unknown nuisance parameters which have to be replaced by estimators, but can be improved by calculating the efficient score, which accounts for the variability induced by estimating these parameters. Manual derivation of the efficient score is tedious and error-prone, so we illustrate using computer algebra to facilitate this derivation. We demonstrate this process within the context of a standard example from genetic association analyses, though the techniques shown here could be applied to any derivation, and have a place in the toolbox of any modern statistician. We further show how the resulting symbolic expressions can be readily ported to compiled languages, to develop fast numerical algorithms for high-throughput genomic analysis. We conclude by considering extensions of this approach. The code featured in this report is available online as part of the supplementary material.     

Time-Varying Systemic Risk: Evidence From a Dynamic Copula Model of CDS Spreads

This article proposes a new class of copula-based dynamic models for high-dimensional conditional distributions, facilitating the estimation of a wide variety of measures of systemic risk. Our proposed models draw on successful ideas from the literature on modeling high-dimensional covariance matrices and on recent work on models for general time-varying distributions. Our use of copula-based models enables the estimation of the joint model in stages, greatly reducing the computational burden. We use the proposed new models to study a collection of daily credit default swap (CDS) spreads on 100 U.S. firms over the period 2006 to 2012. We find that while the probability of distress for individual firms has greatly reduced since the financial crisis of 2008–2009, the joint probability of distress (a measure of systemic risk) is substantially higher now than in the precrisis period. Supplementary materials for this article are available online.     

Efficient $$\hbox {SMC}^2$$ schemes for stochastic kinetic models

Fitting stochastic kinetic models represented by Markov jump processes within the Bayesian paradigm is complicated by the intractability of the observed-data likelihood. There has therefore been considerable attention given to the design of pseudo-marginal Markov chain Monte Carlo algorithms for such models. However, these methods are typically computationally intensive, often require careful tuning and must be restarted from scratch upon receipt of new observations. Sequential Monte Carlo (SMC) methods on the other hand aim to efficiently reuse posterior samples at each time point. Despite their appeal, applying SMC schemes in scenarios with both dynamic states and static parameters is made difficult by the problem of particle degeneracy. A principled approach for overcoming this problem is to move each parameter particle through a Metropolis-Hastings kernel that leaves the target invariant. This rejuvenation step is key to a recently proposed \(\hbox {SMC}^2\) algorithm, which can be seen as the pseudo-marginal analogue of an idealised scheme known as iterated batch importance sampling. Computing the parameter weights in \(\hbox {SMC}^2\) requires running a particle filter over dynamic states to unbiasedly estimate the intractable observed-data likelihood up to the current time point. In this paper, we propose to use an auxiliary particle filter inside the \(\hbox {SMC}^2\) scheme. Our method uses two recently proposed constructs for sampling conditioned jump processes, and we find that the resulting inference schemes typically require fewer state particles than when using a simple bootstrap filter. Using two applications, we compare the performance of the proposed approach with various competing methods, including two global MCMC schemes.     

Critical context and international intrafirm best-practice transfers

The sharing and transfer of knowledge and best practices across the organization have long been recognized as a critical driver of a firm's capabilities and performance. In fact, Gupta and Govindarajan (1991) maintained that MNCs exist primarily because of their superior ability to transfer knowledge internally relative to the ability of markets. This paper examines factors that influence the success of intrafirm, cross-border knowledge transfers. We investigate the critical context similarity between best-practice source and recipient units and the impact of critical context similarity on transfer eventfulness. We argue that best practices are embedded within a set of 5 central contextual elements, which are critical to the firm's ability to utilize them. A model is developed and tested that explains the impact of context in enabling or inhibiting best-practice transfers. Our findings suggest that critical context dissimilarity inhibits best-practice transfers. The impact of congruence among the source and recipient units along the dimensions of culture, strategy, decision-making, environment and technology affects the eventfulness of the transfer in terms of time, budget and satisfaction. Additionally, the effect of the fit between the practice characteristics, and the transfer mechanisms employed, is examined.     

National Styles of Science and Technology: A Comparative Model*

This essay articulates a comparative approach to the sociology of science. It suggests that even historically there have been numerous scholars interested in the national components of scientific knowledge. I develop a model which allows me to compare the differences in scientific growth in Sweden and Denmark. My model consists of three interrelated levels: metaphysical bias, national scientific interests, institutional structures. The comparison between the two countries shows them to be markedly different on all three levels. Finally, I suggest how the model can be applied to contemporary material.     

Family Therapy and the Theory of Logical Types

This paper re-examines the theory of logical types as it relates to family therapy. It examines the problems of Russell and Whitehead's original theory and the way in which it can provide an understanding of the nature of psychotherapy. Recent theoretical advances are discussed and some techniques specifically informed by the theory of logical types are discussed with reference to case material.     

The effect of task differences,attention and personality on the frequency of body-focused hand movements

A hypothesized relationship between apparently irrelevant self or object manipulatory hand movements and attentional demand was investigated by observing the frequency of these hand movements during the performance of three tasks: a monologue, mental arithmetic, and rest. The attentional demand of each task was assessed by means of a simultaneous reaction time probe task. The monologue task had both the highest frequency of hand movements and the highest level of attentional demand. However, correlations between these measures were not significant for any of the tasks. The influence of the task differences on movement frequencies was much greater than the effect of individual differences. Correlations of trait anxiety and field dependence measures with movement frequencies varied markedly with task differences. The nature and significance of these movements is considered by reference to similar observations in nonhuman species.