scholarly journals Trajetória de casos de hanseníase e fatores relacionados/Trajectory of leprosy cases and related factors

2018 ◽  
Vol 17 (3) ◽  
Author(s):  
Cosme Rezende Laurino ◽  
Sarah Lamas Vidal ◽  
Bernadete Marinho Bara De Martin Gama ◽  
Liliany Fontes Loures ◽  
Gilmara Aparecida Batista Fernandes ◽  
...  
Keyword(s):  
Social Sciences ◽  
Open Data ◽  
Related Factors ◽  
The Social ◽  
Open Data Kit

Objetivou-se analisar a trajetória dos casos diagnosticados com hanseníase e os fatores relacionados, com enfoque nas ações de prevenção e controle preconizadas pelo Ministério da Saúde. Estudo transversal de natureza descritiva. Os dados foram coletados através de questionário estruturado em visitas domiciliares, com uso do programa Open Data Kit collect. Foram exportados, tratados e analisados no Statistical Package for the Social Sciences (SPSS®) v. 24. Para o cálculo do diagnóstico tardio considerou-se três variáveis, bastando a presença de uma: passar por mais de um serviço de saúde; mais de uma vez; ou mais de seis meses entre primeiros sinais e sintomas até o diagnóstico efetivo. Dos participantes: 69,6% aos primeiros sintomas procuraram o serviço de saúde mais próximo; 43,5% procuraram a Unidade Básica de Saúde de referência; 73,9% relataram demora menor ou igual a um mês para consulta; 34,8% diagnosticados em ambulatório de referência; 82,6% diagnosticados tardiamente; não houve participação em sala de espera ou grupo educativo com o tema hanseníase em unidades básicas de saúde de referência; 21,7% negaram acesso a orientações ao autocuidado. Percebeu-se dificuldade operacional evidenciada por diagnóstico tardio e inadequação das ações de prevenção e controle da hanseníase preconizadas pelo Ministério da Saúde.

scholarly journals Computational Social Science of Disasters: Opportunities and Challenges

2019 ◽  
Vol 11 (5) ◽  
pp. 103 ◽  
Author(s):  
Burger ◽  
Oz ◽  
Kennedy ◽  
Crooks
Keyword(s):  
Social Sciences ◽  
Social Science ◽  
Data Science ◽  
Open Data ◽  
Computational Social Science ◽  
Crisis Informatics ◽  
Disaster Research ◽  
Social Responses ◽  
Digital Revolution ◽  
The Social

Disaster events and their economic impacts are trending, and climate projection studies suggest that the risks of disaster will continue to increase in the near future. Despite the broad and increasing social effects of these events, the empirical basis of disaster research is often weak, partially due to the natural paucity of observed data. At the same time, some of the early research regarding social responses to disasters have become outdated as social, cultural, and political norms have changed. The digital revolution, the open data trend, and the advancements in data science provide new opportunities for social science disaster research. We introduce the term computational social science of disasters (CSSD), which can be formally defined as the systematic study of the social behavioral dynamics of disasters utilizing computational methods. In this paper, we discuss and showcase the opportunities and the challenges in this new approach to disaster research. Following a brief review of the fields that relate to CSSD, namely traditional social sciences of disasters, computational social science, and crisis informatics, we examine how advances in Internet technologies offer a new lens through which to study disasters. By identifying gaps in the literature, we show how this new field could address ways to advance our understanding of the social and behavioral aspects of disasters in a digitally connected world. In doing so, our goal is to bridge the gap between data science and the social sciences of disasters in rapidly changing environments.

scholarly journals Data Access for the Social Sciences in Austria. Open Data, Closed Data, Research Infrastructures and Re-Use

2021 ◽  
Vol 50 (1) ◽  
pp. 15
Author(s):  
Matthias Reiter-Pázmándy
Keyword(s):  
Social Sciences ◽  
Research Policy ◽  
Open Data ◽  
Data Access ◽  
Open Science ◽  
Research Data ◽  
The Social ◽  
Research Infrastructures ◽  
Barriers And Challenges ◽  
Access To Data

Open science and open access to research data are important aspects of research policy in Austria. In the last years, the social sciences have seen the building of research infrastructures that generate data and archives that store data. Data standards have been established, several working groups exist and a number of activities aim to further develop various aspects of open science, open data and access to data. However, some barriers and challenges still exist in the practice of sharing research data. One aspect that should be emphasised and incentivised is the re-use of research data.

scholarly journals From Big Data to Knowledge in the Social Sciences

2015 ◽  
Vol 659 (1) ◽  
pp. 16-32 ◽  
Author(s):  
Bradford W. Hesse ◽  
Richard P. Moser ◽  
William T. Riley
Keyword(s):  
Social Sciences ◽  
Big Data ◽  
Open Data ◽  
High Volume ◽  
National Institutes Of Health ◽  
Scientific Enterprise ◽  
Data Intensive ◽  
Scientific Disciplines ◽  
The Social ◽  
Systemic Problems

One of the challenges associated with high-volume, diverse datasets is whether synthesis of open data streams can translate into actionable knowledge. Recognizing that challenge and other issues related to these types of data, the National Institutes of Health developed the Big Data to Knowledge or BD2K initiative. The concept of translating “big data to knowledge” is important to the social and behavioral sciences in several respects. First, a general shift to data-intensive science will exert an influence on all scientific disciplines, but particularly on the behavioral and social sciences given the wealth of behavior and related constructs captured by big data sources. Second, science is itself a social enterprise; by applying principles from the social sciences to the conduct of research, it should be possible to ameliorate some of the systemic problems that plague the scientific enterprise in the age of big data. We explore the feasibility of recalibrating the basic mechanisms of the scientific enterprise so that they are more transparent and cumulative; more integrative and cohesive; and more rapid, relevant, and responsive.

Linking Knowledge

2021 ◽  
Keyword(s):  
Social Sciences ◽  
Data Mining ◽  
Semantic Web ◽  
Cultural Heritage ◽  
Open Data ◽  
Linked Open Data ◽  
Social Sciences And Humanities ◽  
The Social ◽  
Knowledge Organization Systems ◽  
Heterogeneous Knowledge

The growth and population of the Semantic Web, especially the Linked Open Data (LOD) Cloud, has brought to the fore the challenges of ordering knowledge for data mining on an unprecedented scale. The LOD Cloud is structured from billions of elements of knowledge and pointers to knowledge organization systems (KOSs) such as ontologies, taxonomies, typologies, thesauri, etc. The variant and heterogeneous knowledge areas that comprise the social sciences and humanities (SSH), including cultural heritage applications are bringing multi-dimensional richness to the LOD Cloud. Each such application arrives with its own challenges regarding KOSs in the Cloud. With contributions by Sören Auer, Gerard Coen, Kathleen Gregory, Mohamad Yaser Jaradeh, Daniel Martínez Ávila, Philipp Mayr, Allard Oelen, Cristina Pattuelli, Tobias Renwick, Andrea Scharnhorst, Ronald Siebes, Aida Slavic, Richard P Smiraglia, Markus Stocker, Rick Szostak, Marnix van Berchum, Charles van den Heuvel, J. Bradford Young, Veruska Zamborlini and Marcia Zeng.

What Can We Learn From Factorial Surveys About Human Behavior?

Methodology ◽  
2019 ◽  
Vol 15 (1) ◽  
pp. 19-30 ◽  
Author(s):  
Knut Petzold ◽  
Tobias Wolbring
Keyword(s):  
Social Sciences ◽  
Behavioral Intentions ◽  
Actual Behavior ◽  
Full Time ◽  
Factorial Survey ◽  
Survey Experiment ◽  
Intended Behavior ◽  
The Social ◽  
Survey Experiments ◽  
High Level

Abstract. Factorial survey experiments are increasingly used in the social sciences to investigate behavioral intentions. The measurement of self-reported behavioral intentions with factorial survey experiments frequently assumes that the determinants of intended behavior affect actual behavior in a similar way. We critically investigate this fundamental assumption using the misdirected email technique. Student participants of a survey were randomly assigned to a field experiment or a survey experiment. The email informs the recipient about the reception of a scholarship with varying stakes (full-time vs. book) and recipient’s names (German vs. Arabic). In the survey experiment, respondents saw an image of the same email. This validation design ensured a high level of correspondence between units, settings, and treatments across both studies. Results reveal that while the frequencies of self-reported intentions and actual behavior deviate, treatments show similar relative effects. Hence, although further research on this topic is needed, this study suggests that determinants of behavior might be inferred from behavioral intentions measured with survey experiments.

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