Family orientated delivery of routine nursing care in hospital

Hospital nursing may be better deployed to acute clinical patient care. The recruitment of family assistance will facilitate this process in patients in hospital awaiting placement and without acute care issues.

Special issue on advances in data mining and its applications

Data mining is the process to identify valid, implicit, previously unknown, potentially useful and understandable information from large databases. It is an important step in the process of knowledge discovery in databases, (Olaru & Wehenkel, 1999). In a data mining process, input data can be structured, seme-structured, or unstructured. Data can be in text, categorical or numerical values. One of the important characteristics of data mining is its ability to deal data with large volume, distributed, time variant, noisy, and high dimensionality. A large number of data mining algorithms have been developed for different applications. For example, association rules mining can be useful for market basket problems, clustering algorithms can be used to discover trends in unsupervised learning problems, classification algorithms can be applied in decision-making problems, and sequential and time series mining algorithms can be used in predicting events, fault detection, and other supervised learning problems (Vapnik, 1999). Classification is among the most important tasks in the data mining, particularly for data mining applications into engineering fields. Together with regression, classification is mainly for predictive modelling. So far, there have been a number of classification algorithms in practice. According to (Sebastiani, 2002), the main classification algorithms can be categorized as: decision tree and rule based approach such as C4.5 (Quinlan, 1996); probability methods such as Bayesian classifier (Lewis, 1998); on-line methods such as Winnow (Littlestone, 1988) and CVFDT (Hulten 2001), neural networks methods (Rumelhart, Hinton & Wiliams, 1986); example-based methods such as k-nearest neighbors (Duda & Hart, 1973), and SVM (Cortes & Vapnik, 1995). Other important techniques for classification tasks include Associative Classification (Liu et al, 1998) and Ensemble Classification (Tumer, 1996).

Reducing problem behavior during care-giving in families of preschool-aged children with developmental disabilities

This study evaluated two variants of a behavioral parent training program known as Stepping Stones Triple P (SSTP) using 74 preschool-aged children with developmental disabilities. Families were randomly allocated to an enhanced parent training intervention that combined parenting skills and care-giving coping skills (SSTP-E), standard parent training intervention alone (SSTP-S) or waitlist control (WL) condition. At post-intervention, both programs were associated with lower levels of observed negative child behavior, reductions in the number of care-giving settings where children displayed problem behavior, and improved parental competence and satisfaction in the parenting role as compared with the waitlist condition. Gains attained at post-intervention were maintained at 1-year follow-up. Both interventions produced significant reductions in child problem behavior, with 67% of children in the SSTP-E and 77% of children in the SSTPS showing clinically reliable change from pre-intervention to follow-up. Parents reported a high level of satisfaction with both interventions.