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Information Technologies represent a primary instrument in all types of organizations. However, their use is not always well planned in the public sector. The research proposes a methodology to build a realistic action plan that improves the use of information technology and its governance in public organizations. We conduct a case study using semi-structured interviews to investigate the different views of 8 decision-makers regarding the use of IT and its governance. The findings were analyzed systematically and synthesized into a set of perspectives on the subject. An interference matrix reveals how each view interferes and receives positive and/or negative interference from the others. Based on the matrix, the methodology applies criteria to prioritize the perspectives with the highest potential to improve the use of IT and its governance. The final product of the methodology is an action plan aligned with the vision of the institution's decision-makers. The plan requires little effort to improve the use of IT and its governance. Seventeen views related to the use of IT and its governance emerged from the interviews. Four views that do not cause or receive harmful interference from other perspectives were selected to guide the plan elaboration. The methodology proved to be efficient for creating an action plan adapted to the institutional reality. According to the managers, the action plan represents with high precision the most urgent needs of the organization, respecting its work capacity and available resources for a project to improve the use of IT and its governance. [ABSTRACT FROM AUTHOR]

The prevalence of health problems during childhood and adolescence is high in developing countries such as Brazil. Social inequality, violence, and malnutrition have strong impact on youth health. To better understand these issues we propose to combine machine-learning methods and graph analysis to build predictive networks applied to the Brazilian National Student Health Survey (PenSE 2015) data, a large dataset that consists of questionnaires filled by the students. By using a combination of gradient boosting machines and centrality hub metric, it was possible to identify potential confounders to be considered when conducting association analyses among variables. The variables were ranked according to their hub centrality to predict the other variables from a directed weighted-graph perspective. The top five ranked confounder variables were "gender", "oral health care", "intended education level", and two variables associated with nutrition habits-"eat while watching TV" and "never eat fast-food". In conclusion, although causal effects cannot be inferred from the data, we believe that the proposed approach might be a useful tool to obtain novel insights on the association between variables and to identify general factors related to health conditions.