Questionnaire of chronic illness care in primary care-psychometric properties and test-retest reliability

<p>Abstract</p> <p>Background</p> <p>The Chronic Care Model (CCM) is an evidence-based approach to improving the structure of care for chronically ill patients with multimorbidity. The Assessment of Chronic Illness Care (ACIC), an instrument commonly used in international research, includes all aspects of the CCM, but cannot be easily extended to the German context. A new instrument called the "Questionnaire of Chronic Illness Care in Primary Care" (QCPC) was developed for use in Germany for this reason. Here, we present the results of the psychometric properties and test-retest reliability of QCPC.</p> <p>Methods</p> <p>A total of 109 family doctors from different German states participated in the validation study. Participating physicians completed the QCPC, which includes items concerning the CCM and practice structure, at baseline (T0) and 3 weeks later (T1). Internal consistency reliability and test-retest reliability were evaluated using Cronbach's alpha and Pearson's r, respectively.</p> <p>Results</p> <p>The QCPC contains five elements of the CCM (decision support, delivery system design, self-management support, clinical information systems, and community linkages). All subscales demonstrated moderate internal consistency and moderate test-retest reliability over a three-week interval.</p> <p>Conclusions</p> <p>The QCPC is an appropriate instrument to assess the structure of chronic illness care. Unlike the ACIC, the QCPC can be used by health care providers without CCM training. The QCPC can detect the actual state of care as well as areas for improvement of care according to the CCM.</p

Evaluation of job satisfaction of practice staff and general practitioners: an exploratory study

Contains fulltext : 95875.pdf (publisher's version ) (Open Access

Climate Informatics

The impacts of present and potential future climate change will be one of the most important scientific and societal challenges in the 21st century. Given observed changes in temperature, sea ice, and sea level, improving our understanding of the climate system is an international priority. This system is characterized by complex phenomena that are imperfectly observed and even more imperfectly simulated. But with an ever-growing supply of climate data from satellites and environmental sensors, the magnitude of data and climate model output is beginning to overwhelm the relatively simple tools currently used to analyze them. A computational approach will therefore be indispensable for these analysis challenges. This chapter introduces the fledgling research discipline climate informatics: collaborations between climate scientists and machine learning researchers in order to bridge this gap between data and understanding. We hope that the study of climate informatics will accelerate discovery in answering pressing questions in climate science

Climate Dynamics: A Network-Based Approach for the Analysis of Global Precipitation

Precipitation is one of the most important meteorological variables for defining the climate dynamics, but the spatial patterns of precipitation have not been fully investigated yet. The complex network theory, which provides a robust tool to investigate the statistical interdependence of many interacting elements, is used here to analyze the spatial dynamics of annual precipitation over seventy years (1941-2010). The precipitation network is built associating a node to a geographical region, which has a temporal distribution of precipitation, and identifying possible links among nodes through the correlation function. The precipitation network reveals significant spatial variability with barely connected regions, as Eastern China and Japan, and highly connected regions, such as the African Sahel, Eastern Australia and, to a lesser extent, Northern Europe. Sahel and Eastern Australia are remarkably dry regions, where low amounts of rainfall are uniformly distributed on continental scales and small-scale extreme events are rare. As a consequence, the precipitation gradient is low, making these regions well connected on a large spatial scale. On the contrary, the Asiatic South-East is often reached by extreme events such as monsoons, tropical cyclones and heat waves, which can all contribute to reduce the correlation to the short-range scale only. Some patterns emerging between mid-latitude and tropical regions suggest a possible impact of the propagation of planetary waves on precipitation at a global scale. Other links can be qualitatively associated to the atmospheric and oceanic circulation. To analyze the sensitivity of the network to the physical closeness of the nodes, short-term connections are broken. The African Sahel, Eastern Australia and Northern Europe regions again appear as the supernodes of the network, confirming furthermore their long-range connection structure. Almost all North-American and Asian nodes vanish, revealing that extreme events can enhance high precipitation gradients, leading to a systematic absence of long-range patterns

Local Difference Measures between Complex Networks for Dynamical System Model Evaluation

Acknowledgments We thank Reik V. Donner for inspiring suggestions that initialized the work presented herein. Jan H. Feldhoff is credited for providing us with the STARS simulation data and for his contributions to fruitful discussions. Comments by the anonymous reviewers are gratefully acknowledged as they led to substantial improvements of the manuscript.Peer reviewedPublisher PD

Suspension fluorescence in situ hybridization (S-FISH) combined with automatic detection and laser microdissection for STR profiling of male cells in male/female mixtures

Laser microdissection is a valuable tool for isolating specific cells from mixtures, such as male cells in a mixture with female cells, e.g., in cases of sexual assault. These cells can be stained with Y-chromosome-specific probes. We developed an automatic screening method to detect male cells after fluorescence in situ hybridization in suspension (S-FISH). To simulate forensic casework, the method was tested on female saliva after cataglottis (a kiss involving tongue-to-tongue contact) and on licking traces (swabs of dried male saliva on female skin) even after drying. After isolation of the detected cells, short tandem repeat profiling was performed. Full DNA profiles could consistently be obtained from as little as ten buccal cells. Isolation of five cells resulted in a mean of 98% (SD of 3.4%) of the alleles detected, showing that the developed S-FISH staining had no significant negative influence on DNA recovery and can be used in forensic casework

Computing Vertex-Vertex Dissimilarities Using Random Trees: Application to Clustering in Graphs

International audienceA current challenge in graph clustering is to tackle the issue of complex networks, i.e, graphs with attributed vertices and/or edges. In this paper, we present GraphTrees, a novel method that relies on random decision trees to compute pairwise dissimilarities between vertices in a graph. We show that using different types of trees, it is possible to extend this framework to graphs where the vertices have attributes. While many existing methods that tackle the problem of clustering vertices in an attributed graph are limited to categorical attributes, GraphTrees can handle heterogeneous types of vertex attributes. Moreover, unlike other approaches, the attributes do not need to be preprocessed. We also show that our approach is competitive with well-known methods in the case of non-attributed graphs in terms of quality of clustering, and provides promising results in the case of vertex-attributed graphs. By extending the use of an already well established approach-the random trees-to graphs, our proposed approach opens new research directions, by lever-aging decades of research on this topic

Finding and testing network communities by lumped Markov chains

Identifying communities (or clusters), namely groups of nodes with comparatively strong internal connectivity, is a fundamental task for deeply understanding the structure and function of a network. Yet, there is a lack of formal criteria for defining communities and for testing their significance. We propose a sharp definition which is based on a significance threshold. By means of a lumped Markov chain model of a random walker, a quality measure called "persistence probability" is associated to a cluster. Then the cluster is defined as an "$\alpha$-community" if such a probability is not smaller than $\alpha$. Consistently, a partition composed of $\alpha$-communities is an "$\alpha$-partition". These definitions turn out to be very effective for finding and testing communities. If a set of candidate partitions is available, setting the desired $\alpha$-level allows one to immediately select the $\alpha$-partition with the finest decomposition. Simultaneously, the persistence probabilities quantify the significance of each single community. Given its ability in individually assessing the quality of each cluster, this approach can also disclose single well-defined communities even in networks which overall do not possess a definite clusterized structure