972 resultados para Social problems in literature.


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This article starts by analysing healthcare litigation in Brazil by means of a literature review of articles that contribute with empirical findings on this phenomenon. Based on this review, I argue that health care litigation in Brazil makes the public health system less fair and rational. In the second part of this article, I discuss the three most overarching responses to control the level of litigation and its impact on the public health system: (i) the public hearing held by the Supreme Federal Court and the criteria the court established thereafter; (ii) the recommendations by the National Council of Justice aimed at building courts’ institutional capacity; and (iii) the enactment of the Federal Law 12.401/11, which created a new health technology assessment system. I argue that latter is the best response because it keeps the substantive decisions on the allocation of healthcare resources in the institution that is in the best position to make them. Moreover, this legislation will make the decisions about provision of health treatments more explicit, making easier for courts to control the procedure and the reasons for these decisions.

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This article develops a life-cycle general equilibrium model with heterogeneous agents who make choices of nondurables consumption, investment in homeowned housing and labour supply. Agents retire from an specific age and receive Social Security benefits which are dependant on average past earnings. The model is calibrated, numerically solved and is able to match stylized U.S. aggregate statistics and to generate average life-cycle profiles of its decision variables consistent with data and literature. We also conduct an exercise of complete elimination of the Social Security system and compare its results with the benchmark economy. The results enable us to emphasize the importance of endogenous labour supply and benefits for agents' consumption-smoothing behaviour.

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Social Entrepreneurship (SE) has attracted growing interest from a wide variety of actors over the last 30 years, especially due to a general agreement that it could be an important tool for tackling many of the world’s social ills. In the academic sphere, this growing interest did not translate into a matured field of study. Quite the opposite, a quick look at this literature makes it evident that: SE has been consistently subjected to numerous theoretical discussions and disagreements, especially over the definition of the concept of SE which is often based on a taken-for-granted notion of social change; it has been more systematically investigated in restricted contexts, often leaving aside so called developing/emerging countries like Brazil and especially lacking in-depth qualitative studies; SE literature lags behind SE practices and few studies focus on how SE actually occurs in a daily and bottom-up manner. In order to address such gaps, this thesis examines how social entrepreneurship practices accomplish social change in the context of Brazil. In this investigation I conducted an inductive practice-based, qualitative/ethnographic study in three Non-Governmental Organizations (NGOs) located in different cities in the Brazilian state of São Paulo. Data collection lasted from February 2014 until March 2015 and was mainly done through participant observations and through in-depth unstructured conversations with research participants. Secondary data and documents were also collected whenever available. The participants of this study included a variety of the studied organizations’ stakeholders: two founders, volunteers, employees, donors and beneficiaries. Observation data was kept in fieldnotes, conversations were recorded whenever possible and were later transcribed. Data was analyzed through an iterative thematic analysis. Through this I identified eight recurrent themes in the data: (1) structure; (2) relationship with other organizational actors (sub-themes: relationship with state, relationship with businesses and relationship with other NGOs); (3) beliefs, spirituality and moral authority; (4) social position of participants, (5) stakeholders’ mobilization and participation; (6) feelings; (7) social purpose; and (8) social change. These findings were later discussed under the lens of practice theory, and in this discussion I argue and show that, in the context studied: (a) even though SE embraces a wide variety of different social purposes, they are intertwined with a common notion of social change based on a general understanding and aspiration for social equality; (b) this social change is accomplished in a processual and ongoing manner as stakeholders from antagonistic social groups felt compelled to and participated in SE practices. In answering the proposed research question the contributions of this thesis are: (i) the elaboration a working definition for SE based on its relationship with social change; (ii) providing in-depth empirical evidence which accounts for and explains this relationship; (iii) characterizing SE in the Brazilian context and reflecting upon its transferability to other contexts. This thesis also makes a methodological contribution, for it demonstrates how thematic analysis can be used in practice-based studies.

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RODRIGUES, Katamara et al. Prevalence of orofacial clefts and social factors in Brazil. Brazilian oral research, v.23, n. 1, p. 38-42, 2009.Disponivel em: . Acesso em: 04 out. 2010.

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Includes bibliography

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Includes bibliography

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Includes bibliography

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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A large historiographic tradition has studied the Brazilian state, yet we know relatively little about its internal dynamics and particularities. The role of informal, personal, and unintentional ties has remained underexplored in most policy network studies, mainly because of the pluralist origin of that tradition. It is possible to use network analysis to expand this knowledge by developing mesolevel analysis of those processes. This article proposes an analytical framework for studying networks inside policy communities. This framework considers the stable and resilient patterns that characterize state institutions, especially in contexts of low institutionalization, particularly those found in Latin America and Brazil. The article builds on research on urban policies in Brazil to suggest that networks made of institutional and personal ties structure state organizations internally and insert them,into broader political scenarios. These networks, which I call state fabric, frame politics, influence public policies, and introduce more stability and predictability than the majority of the literature usually considers. They also form a specific power resource-positional power, associated with the positions that political actors occupy-that influences politics inside and around the state.

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The living conditions of the inhabitants of Iauarete, an indigenous area in the municipality of Sao Gabriel da Cachoeira, State of Amazonas (Northern Brazil), have been negatively affected by population density, poor sanitation and maintenance of sanitation practices that are incompatible with that reality. To improve the population's quality of life, sanitation systems that are adequate to the local socio-cultural characteristics should be implemented, as well as educational processes with emphasis on social mobilization and community empowerment. The aim of this paper is to report and discuss a training course on health and sanitation using action research, directed to the mobilization of the Iauarete indigenous people, with the objective of assisting other studies of this nature. In the meetings, issues related to environmental health were discussed, a Community Newspaper was constructed, the course participants made interviews and drew up claims documents. This experience has enhanced the participants' understanding of local problems and of the importance of social mobilization for the dialogue with governmental institutions that are responsible for providing sanitation services and for seeking better living conditions. The researchers and teachers of the training course benefitted from the construction of collective knowledge resulting from interaction with subjects of the investigated situation and from the recognition and redefinition of their representations, fulfilling the fundamental premise of action research.

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In the past decade, the advent of efficient genome sequencing tools and high-throughput experimental biotechnology has lead to enormous progress in the life science. Among the most important innovations is the microarray tecnology. It allows to quantify the expression for thousands of genes simultaneously by measurin the hybridization from a tissue of interest to probes on a small glass or plastic slide. The characteristics of these data include a fair amount of random noise, a predictor dimension in the thousand, and a sample noise in the dozens. One of the most exciting areas to which microarray technology has been applied is the challenge of deciphering complex disease such as cancer. In these studies, samples are taken from two or more groups of individuals with heterogeneous phenotypes, pathologies, or clinical outcomes. these samples are hybridized to microarrays in an effort to find a small number of genes which are strongly correlated with the group of individuals. Eventhough today methods to analyse the data are welle developed and close to reach a standard organization (through the effort of preposed International project like Microarray Gene Expression Data -MGED- Society [1]) it is not unfrequant to stumble in a clinician's question that do not have a compelling statistical method that could permit to answer it.The contribution of this dissertation in deciphering disease regards the development of new approaches aiming at handle open problems posed by clinicians in handle specific experimental designs. In Chapter 1 starting from a biological necessary introduction, we revise the microarray tecnologies and all the important steps that involve an experiment from the production of the array, to the quality controls ending with preprocessing steps that will be used into the data analysis in the rest of the dissertation. While in Chapter 2 a critical review of standard analysis methods are provided stressing most of problems that In Chapter 3 is introduced a method to adress the issue of unbalanced design of miacroarray experiments. In microarray experiments, experimental design is a crucial starting-point for obtaining reasonable results. In a two-class problem, an equal or similar number of samples it should be collected between the two classes. However in some cases, e.g. rare pathologies, the approach to be taken is less evident. We propose to address this issue by applying a modified version of SAM [2]. MultiSAM consists in a reiterated application of a SAM analysis, comparing the less populated class (LPC) with 1,000 random samplings of the same size from the more populated class (MPC) A list of the differentially expressed genes is generated for each SAM application. After 1,000 reiterations, each single probe given a "score" ranging from 0 to 1,000 based on its recurrence in the 1,000 lists as differentially expressed. The performance of MultiSAM was compared to the performance of SAM and LIMMA [3] over two simulated data sets via beta and exponential distribution. The results of all three algorithms over low- noise data sets seems acceptable However, on a real unbalanced two-channel data set reagardin Chronic Lymphocitic Leukemia, LIMMA finds no significant probe, SAM finds 23 significantly changed probes but cannot separate the two classes, while MultiSAM finds 122 probes with score >300 and separates the data into two clusters by hierarchical clustering. We also report extra-assay validation in terms of differentially expressed genes Although standard algorithms perform well over low-noise simulated data sets, multi-SAM seems to be the only one able to reveal subtle differences in gene expression profiles on real unbalanced data. In Chapter 4 a method to adress similarities evaluation in a three-class prblem by means of Relevance Vector Machine [4] is described. In fact, looking at microarray data in a prognostic and diagnostic clinical framework, not only differences could have a crucial role. In some cases similarities can give useful and, sometimes even more, important information. The goal, given three classes, could be to establish, with a certain level of confidence, if the third one is similar to the first or the second one. In this work we show that Relevance Vector Machine (RVM) [2] could be a possible solutions to the limitation of standard supervised classification. In fact, RVM offers many advantages compared, for example, with his well-known precursor (Support Vector Machine - SVM [3]). Among these advantages, the estimate of posterior probability of class membership represents a key feature to address the similarity issue. This is a highly important, but often overlooked, option of any practical pattern recognition system. We focused on Tumor-Grade-three-class problem, so we have 67 samples of grade I (G1), 54 samples of grade 3 (G3) and 100 samples of grade 2 (G2). The goal is to find a model able to separate G1 from G3, then evaluate the third class G2 as test-set to obtain the probability for samples of G2 to be member of class G1 or class G3. The analysis showed that breast cancer samples of grade II have a molecular profile more similar to breast cancer samples of grade I. Looking at the literature this result have been guessed, but no measure of significance was gived before.

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In this thesis we made the first steps towards the systematic application of a methodology for automatically building formal models of complex biological systems. Such a methodology could be useful also to design artificial systems possessing desirable properties such as robustness and evolvability. The approach we follow in this thesis is to manipulate formal models by means of adaptive search methods called metaheuristics. In the first part of the thesis we develop state-of-the-art hybrid metaheuristic algorithms to tackle two important problems in genomics, namely, the Haplotype Inference by parsimony and the Founder Sequence Reconstruction Problem. We compare our algorithms with other effective techniques in the literature, we show strength and limitations of our approaches to various problem formulations and, finally, we propose further enhancements that could possibly improve the performance of our algorithms and widen their applicability. In the second part, we concentrate on Boolean network (BN) models of gene regulatory networks (GRNs). We detail our automatic design methodology and apply it to four use cases which correspond to different design criteria and address some limitations of GRN modeling by BNs. Finally, we tackle the Density Classification Problem with the aim of showing the learning capabilities of BNs. Experimental evaluation of this methodology shows its efficacy in producing network that meet our design criteria. Our results, coherently to what has been found in other works, also suggest that networks manipulated by a search process exhibit a mixture of characteristics typical of different dynamical regimes.