Manuel Au-Yong Oliveira

Abstract
The main goal of this article is to identify the main dimensions of a model proposal for increasing the potential of big data research in Healthcare for medical doctors' (MDs') learning, which appears as a major issue in continuous medical education and learning. The paper employs a systematic literature review of main scientific databases (PubMed and Google Scholar), using the VOSviewer software tool, which enables the visualization of scientific landscapes. The analysis includes a co-authorship data analysis as well as the co-occurrence of terms and keywords. The results lead to the construction of the learning model proposed, which includes four health big data key areas for MDs' learning: 1) data transformation is related to the learning that occurs through medical systems; 2) health intelligence includes the learning regarding health innovation based on predictions and forecasting processes; 3) data leveraging regards the learning about patient information; and 4) the learning process is related to clinical decision-making, focused on disease diagnosis and methods to improve treatments. Practical models gathered from the scientific databases can boost the learning process and revolutionise the medical industry, as they store the most recent knowledge and innovative research.

Abstract
Burnout is increasingly present in organizations and in the most diverse professions, namely, in university students. Burnout can have negative repercussions on their well-being and can even lead them to abandon their studies. The objective of the study focuses on academic burnout and taking medication as a consequence of the requirements of the academic path of students at a Portuguese public university. To achieve this goal, a quantitative methodology was used, consisting of the distribution of a questionnaire to a sample of students from the analyzed university. The first study questionnaire obtained 207 responses, all valid. To perform the analysis of the quantitative data, the program IBM SPSS Statistics, version 25 was used. Inferential statistics were used, namely, Student t-test and one-way ANOVA (parametric tests), Spearman's correlation coefficient, and the Chi-square test, to test the previously defined research hypotheses. Among the variables for which statistically significant relationships with burnout were found, the following stand out: the arithmetic mean (course average); the professional situation; participation in extracurricular activities; the practice and frequency of physical exercise; the choice and expectations regarding the course; the uncertainty felt about the professional future; the evaluation of the relationship with colleagues.

Abstract

Abstract
As information systems and technologies grow in usage in the agri-food industry, the same has happened to the relevance of Information Systems (IS) that allow for a parallel control, monitoring and management of the organizations' activities and business processes. As the literature proves, the benefits of implementing adequate and interoperable IS are very numerous and tend to represent a significant determinant regarding the organizations' overall success. Despite this, to the best of our knowledge there currently is no IS architecture designed to serve the specificities of the agri-food industry. With this study a novel information systems architecture for the agri-food sector is proposed. The artefact is composed by 12 integrated main components and a set of subcomponents aimed at supporting all the monitoring, control and management activities. In order to validate the proposed architecture a case study was implemented at a mushroom production organization. This allowed us to perceive the ability of our artefact to serve as the basis for the development of IS that address all of the organization's business and environmental needs.

Abstract