Incremental Clinical Utility of ADHD Assessment Measures with Latino Families

Attention-Deficit/Hyperactivity Disorder (ADHD) is a common disorder beginning in childhood, with related symptoms and impairment across settings often persisting into adolescence and adulthood if effective treatment is not provided (Bernardi et al., 2012). Therefore, the early and accurate assessment and diagnosis of ADHD is critical. While the prevalence of ADHD symptomatology has been found to be consistent between Latinos and European Americans (Morgan, Hillemeir, Farkas, & Maczuga, 2014), there is little research on the best practices for assessing ADHD in Latinos. The current study sought to examine the incremental clinical utility of two parent- and teacher-report measures of ADHD symptomatology and functional impairment used to assess ADHD in a sample of Latino children. A sample of Latino schoolchildren (N=53) was recruited to participate in the current study, along with their primary parents and teachers; a comprehensive ADHD assessment was conducted for each participant. Results suggest that teachers in the current sample had a higher rate of agreement with final clinical judgment than did parents in the current sample. Additionally, results suggest that parent- and teacher-reports of functional impairment did not add incremental utility in predicting ADHD diagnostic status, beyond that of parent- and teacher-reports of ADHD symptomatology; follow-up analyses suggest why this may be the case. Lastly, results suggest that teacher-reports of ADHD symptoms and functional impairment added incremental utility in predicting ADHD diagnostic status, beyond parent-reports of ADHD symptoms and functional impairment. Clinical implications of these findings will be discussed

Forging partnerships in health care: Process and measuring benefits

Universally, there is concern that much academic learning has dealt mainly in theory, removing knowledge from context with a resultant lack of practical experience. Here, the catalyst for strengthening university-community engagement, emanated from a desire to foster greater propensity within students to make connections between their academic courses and responsibility toward the community and people in need, and thus develop enhanced skills in social interaction, teamwork and effectiveness. This paper explores a variety of models of university-community engagement that aim to achieve and model good practice in policy making and planning around healthcare education and service development. Ways of integrating teaching and learning with community engagement, so there is reciprocal learning with significant benefits to the community, students, the university and industry are described. The communities of engagement for a transdisciplinary approach in healthcare are defined and the types of collaborative partnerships are outlined, including public/private partnerships, service learning approaches and regional campus engagement. The processes for initiating innovation in this field, forging sustainable partnerships, providing cooperative leadership and building shared vision are detailed. Measuring shared and sustained benefits for all participants is examined in the context of effecting changes in working relationships as well as the impact on students in terms of increased personal and social responsibility, confidence and competence. For the health professions, it is considered vital to adopt this approach in order to deliver graduates who feel aware of community needs, believe they can make a difference, and have a greater sense of community responsibility, ethic of service and more sophisticated understandings of social contexts. In the longer term, it is proposed the strategy will deliver a future healthcare workforce that is more likely to have a strengthened sense of community, social and personal responsibility and thus effect positive social change

Predicting and Explaining Human Semantic Search in a Cognitive Model

Recent work has attempted to characterize the structure of semantic memory and the search algorithms which, together, best approximate human patterns of search revealed in a semantic fluency task. There are a number of models that seek to capture semantic search processes over networks, but they vary in the cognitive plausibility of their implementation. Existing work has also neglected to consider the constraints that the incremental process of language acquisition must place on the structure of semantic memory. Here we present a model that incrementally updates a semantic network, with limited computational steps, and replicates many patterns found in human semantic fluency using a simple random walk. We also perform thorough analyses showing that a combination of both structural and semantic features are correlated with human performance patterns.Comment: To appear in proceedings for CMCL 201

A Guide to Measuring Advocacy and Policy

The overall purpose of this guide is twofold. To help grantmakers think about and talk about measurement of advocacy and policy, this guide puts forth a framework for naming outcomes associated with advocacy and policy work as well as directions for evaluation design. The framework is intended to provide a common way to identify and talk about outcomes, providing philanthropic and non-profit audiences an opportunity to react to, refine and adopt the outcome categories presented. In addition, grantmakers can consider some key directions for evaluation design that include a broad range of methodologies, intensities, audiences, timeframes and purposes. Included in the guide are a tool to measure improved policies, a tool to measure a strengthened base of public support, and a survey to measure community members' perceptions about the prioritization of issues

Population-based incremental learning with associative memory for dynamic environments

Copyright © 2007 IEEE. Reprinted from IEEE Transactions on Evolutionary Computation. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.In recent years there has been a growing interest in studying evolutionary algorithms (EAs) for dynamic optimization problems (DOPs) due to its importance in real world applications. Several approaches, such as the memory and multiple population schemes, have been developed for EAs to address dynamic problems. This paper investigates the application of the memory scheme for population-based incremental learning (PBIL) algorithms, a class of EAs, for DOPss. A PBIL-specific associative memory scheme, which stores best solutions as well as corresponding environmental information in the memory, is investigated to improve its adaptability in dynamic environments. In this paper, the interactions between the memory scheme and random immigrants, multi-population, and restart schemes for PBILs in dynamic environments are investigated. In order to better test the performance of memory schemes for PBILs and other EAs in dynamic environments, this paper also proposes a dynamic environment generator that can systematically generate dynamic environments of different difficulty with respect to memory schemes. Using this generator a series of dynamic environments are generated and experiments are carried out to compare the performance of investigated algorithms. The experimental results show that the proposed memory scheme is efficient for PBILs in dynamic environments and also indicate that different interactions exist between the memory scheme and random immigrants, multi-population schemes for PBILs in different dynamic environments

Best-Choice Edge Grafting for Efficient Structure Learning of Markov Random Fields

Incremental methods for structure learning of pairwise Markov random fields (MRFs), such as grafting, improve scalability by avoiding inference over the entire feature space in each optimization step. Instead, inference is performed over an incrementally grown active set of features. In this paper, we address key computational bottlenecks that current incremental techniques still suffer by introducing best-choice edge grafting, an incremental, structured method that activates edges as groups of features in a streaming setting. The method uses a reservoir of edges that satisfy an activation condition, approximating the search for the optimal edge to activate. It also reorganizes the search space using search-history and structure heuristics. Experiments show a significant speedup for structure learning and a controllable trade-off between the speed and quality of learning

Technological learning for innovating towards sustainable cultivation practices: the Vietnamese smallholder rose sector

Deregulation and globalisation has altered the views of public involvement in development and led to strategies focusing on private sector participation. An implicit assumption seems to be that these linkages will enhance the technological capacity of smallholder producers by way of more cost-efficient technologies trickling down through the value chain or by quality requirements inducing best practices. The argument put forward in this paper is that sustainable non traditional agricultural chain development requires more purposeful actions and institutional transitions, both in the public and private spheres, targeting improved upstream innovative capacities. Empirical findings from a Dutch-Vietnamese partnership on sustainable floriculture development are used. Research revealed that the pest and disease control solutions applied by smallholder rose growers were incremental adaptations of experiences obtained in former food crop cultivation practices. Floriculture however may require more drastic changes in cultivation practices to make the sector more environmentally benign. In the case of smallholder Vietnamese flower producers, this implies adaptation of knowledge and skills currently not present. An important hindrance in promoting this knowledge and skills appears to be the weak vertical linkages between flower growers and public and private research and development organizations

Cache Hierarchy Inspired Compression: a Novel Architecture for Data Streams

We present an architecture for data streams based on structures typically found in web cache hierarchies. The main idea is to build a meta level analyser from a number of levels constructed over time from a data stream. We present the general architecture for such a system and an application to classification. This architecture is an instance of the general wrapper idea allowing us to reuse standard batch learning algorithms in an inherently incremental learning environment. By artificially generating data sources we demonstrate that a hierarchy containing a mixture of models is able to adapt over time to the source of the data. In these experiments the hierarchies use an elementary performance based replacement policy and unweighted voting for making classification decisions