Child Health Partnerships: a review of program characteristics, outcomes and their relationship

<p>Abstract</p> <p>Background</p> <p>Novel approaches are increasingly employed to address the social determinants of health of children world-wide. Such approaches have included complex social programs involving multiple stakeholders from different sectors jointly working together (hereafter Child Health Partnerships). Previous reviews have questioned whether these programs have led to significant improvements in child health and related outcomes. We aim to provide definitive answers to this question as well as identifying the characteristics of successful partnerships.</p> <p>Methods</p> <p>A comprehensive literature search identified 11 major Child Health Partnerships in four comparable developed countries. A critical review is focused on various aspects of these including their target groups, program mechanics and outcomes.</p> <p>Results and Conclusions</p> <p>There was evidence of success in several major areas from the formation of effective joint operations of partners in different partnership models to improvement in both child wellbeing and parenting. There is emerging evidence that Child Health Partnerships are cost-effective. Population characteristics and local contexts need to be taken into account in the introduction and implementation of these programs.</p

Supervised versus unsupervised categorization: Two sides of the same coin?

Supervised and unsupervised categorization have been studied in separate research traditions. A handful of studies have attempted to explore a possible convergence between the two. The present research builds on these studies, by comparing the unsupervised categorization results of Pothos et al. (submitted; 2008) with the results from two procedures of supervised categorization. In two experiments, we tested 375 participants with nine different stimulus sets, and examined the relation between ease of learning of a classification, memory for a classification, and spontaneous preference for a classification. After taking into account the role of the number of category labels (clusters) in supervised learning, we found the three variables to be closely associated with each other. Our results provide encouragement for researchers seeking unified theoretical explanations for supervised and unsupervised categorization, but raise a range of challenging theoretical questions

Understanding interactions in face-to-face and remote undergraduate science laboratories

This paper reviews the ways in which interactions have been studied, and the findings of such studies, in science education in both face-to-face and remote laboratories. Guided by a systematic selection process, 27 directly relevant articles were analysed based on three categories: the instruments used for measuring interactions, the research findings on student interactions, and the theoretical frameworks used in the studies of student interactions. In face-to-face laboratories, instruments for measuring interactions and the characterisation of the nature of interactions were prominent. For remote laboratories, the analysis of direct interactions was found to be lacking. Instead, studies of remote laboratories were mainly concerned with their practical scope. In addition, it is found that only a limited number of theoretical frameworks have been developed and applied in the research design. Existent theories are summarised and possible theoretical frameworks that may be implemented in studies of interactions in undergraduate laboratories are proposed. Finally, future directions for research on the interrelationship between student interactions and laboratory learning are suggested

A nonspatial methodology for the analysis of two-way proximity data incorporating the distance-density hypothesis

This paper presents a nonspatial operationalization of the Krumhansl (1978, 1982) distancedensity model of similarity. This model assumes that the similarity between two objects i and j is a function of both the interpoint distance between i and j and the density of other stimulus points in the regions surrounding i and j . We review this conceptual model and associated empirical evidence for such a specification. A nonspatial, tree-fitting methodology is described which is sufficiently flexible to fit a number of competing hypotheses of similarity formation. A sequential, unconstrained minimization algorithm is technically presented together with various program options. Three applications are provided which demonstrate the flexibility of the methodology. Finally, extensions to spatial models, three-way analyses, and hybrid models are discussed.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45746/1/11336_2005_Article_BF02295285.pd

Effects of category diversity on learning, memory, and generalization

In this study, we examined the effect of within-category diversity on people’s ability to learn perceptual categories, their inclination to generalize categories to novel items, and their ability to distinguish new items from old. After learning to distinguish a control category from an experimental category that was either clustered or diverse, participants performed a test of category generalization or old-new recognition. Diversity made learning more difficult, increased generalization to novel items outside the range of training items, and made it difficult to distinguish such novel items from familiar ones. Regression analyses using the generalized context model suggested that the results could be explained in terms of similarities between old and new items combined with a rescaling of the similarity space that varied according to the diversity of the training items. Participants who learned the diverse category were less sensitive to psychological distance than were the participants who learned a more clustered category

Tscale: A new multidimensional scaling procedure based on tversky's contrast model

Tversky's contrast model of proximity was initially formulated to account for the observed violations of the metric axioms often found in empirical proximity data. This set-theoretic approach models the similarity/dissimilarity between any two stimuli as a linear (or ratio) combination of measures of the common and distinctive features of the two stimuli. This paper proposes a new spatial multidimensional scaling (MDS) procedure called TSCALE based on Tversky's linear contrast model for the analysis of generally asymmetric three-way, two-mode proximity data. We first review the basic structure of Tversky's conceptual contrast model. A brief discussion of alternative MDS procedures to accommodate asymmetric proximity data is also provided. The technical details of the TSCALE procedure are given, as well as the program options that allow for the estimation of a number of different model specifications. The nonlinear estimation framework is discussed, as are the results of a modest Monte Carlo analysis. Two consumer psychology applications are provided: one involving perceptions of fast-food restaurants and the other regarding perceptions of various competitive brands of cola soft-drinks. Finally, other applications and directions for future research are mentioned.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45750/1/11336_2005_Article_BF02294658.pd