Contribution mapping: a method for mapping the contribution of research to enhance its impact.

Background: At a time of growing emphasis on both the use of research and accountability, it is important for research funders, researchers and other stakeholders to monitor and evaluate the extent to which research contributes to better action for health, and find ways to enhance the likelihood that beneficial contributions are realized. Past attempts to assess research 'impact' struggle with operationalizing 'impact', identifying the users of research and attributing impact to research projects as source. In this article we describe Contribution Mapping, a novel approach to research monitoring and evaluation that aims to assess contributions instead of impacts. The approach focuses on processes and actors and systematically assesses anticipatory efforts that aim to enhance contributions, so-called alignment efforts. The approach is designed to be useful for both accountability purposes and for assisting in better employing research to contribute to better action for health.Methods: Contribution Mapping is inspired by a perspective from social studies of science on how research and knowledge utilization processes evolve. For each research project that is assessed, a three-phase process map is developed that includes the main actors, activities and alignment efforts during research formulation, production and knowledge extension (e.g. dissemination and utilization). The approach focuses on the actors involved in, or interacting with, a research project (the linked actors) and the most likely influential users, who are referred to as potential key users. In the first stage, the investigators of the assessed project are interviewed to develop a preliminary version of the process map and first estimation of research-related contributions. In the second stage, potential key-users and other informants are interviewed to trace, explore and triangulate possible contributions. In the third stage, the presence and role of alignment efforts is analyzed and the preliminary results are shared with relevant stakeholders for feedback and validation. After inconsistencies are clarified or described, the results are shared with stakeholders for learning, improvement and accountability purposes.Conclusion: Contribution Mapping provides an interesting alternative to existing methods that aim to assess research impact. The method is expected to be useful for research monitoring, single case studies, comparing multiple cases and indicating how research can better be employed to contribute to better action for health. © 2012 Kok and Schuit; licensee BioMed Central Ltd

Atenção à saúde de pessoas em situação de rua: estudo comparado de unidades móveis em Portugal, Estados Unidos e Brasil

Resumo O trabalho descreve e analisa o quadro legal e normativo que orienta o uso de unidades móveis em Portugal, Estados Unidos e Brasil, que buscam melhorar o acesso e a continuidade dos cuidados em saúde de pessoas em situação de rua. Utilizou-se a análise comparada, por meio de revisão bibliográfica e documental relacionando três categorias: contexto (demográfico, socioeconômico e epidemiológico), sistema de serviços (acesso, cobertura, organização, gestão e financiamento) e as unidades móveis especificamente (concepção, modelo de atenção e financiamento). A análise fundamentou-se na teoria da convergência/divergência entre os sistemas de saúde, pela perspectiva da equidade em saúde. A melhoria do acesso, a abordagem do uso abusivo de substâncias psicoativas, busca ativa e trabalho multidisciplinar mostrou-se comuns aos três países, com potencial para reduzir as iniquidades. As relações com a atenção primária, uso de veículos e o tipo de financiamento são consideradas de maneira divergente nos três países, influenciando o maior ou menor alcance da equidade nas propostas analisadas

Optimal policy for labeling training samples

Confirming the labels of automatically classified patterns is generally faster than entering new labels or correcting incorrect labels. Most labels assigned by a classifier, even if trained only on relatively few pre-labeled patterns, are correct. Therefore the overall cost of human labeling can be decreased by interspersing labeling and classification. Given a parameterized model of the error rate as an inverse power law function of the size of the training set, the optimal splits can be computed rapidly. Projected savings in operator time are over 60 % for a range of empirical error functions for hand-printed digit classification with ten different classifiers

Efficient Data Allocation for a Cluster of Workstations

The development and use of cluster based computing is increasingly becoming an effective approach for solving high performance computing problems. The trend of moving away from specialized traditional supercomputing platforms such as Cray / SGI T3E to cheap and general-purpose systems consisting of loosely coupled components is expected to continue. In [1], we presented an analytical model for evaluating the performance of such platforms. The model covers the effect of storage limitations, interconnection networks and the impact of data partitioning and allocation. The model can also detect the bottlenecks in the system, which can lead to a more effective utilization of the available resources. In this paper, we use our model to find the best data allocation method that maximizes the throughput of the system based on the cluster architecture

Comparison of Buffer Usage Utilizing Single and Multiple Servers in Network Systems with Power-Tail Distributions

this paper, we will plot using logarithmic scales, usually multiplying by 1-ae. The curves are discontinuous because N is an integer function, and have negative slopes for small ae because of the factor 1-ae. Figure 5 shows that although the buffer size can become very large as a