Accessing online health content of high quality and reliability presents challenges. Laypersons cannot easily differentiate trustworthy content from misinformed or manipulated content. This article describes complementary approaches for members of the general public and health professionals to find trustworthy content with as little bias as possible. These include the Khresmoi health search engine (K4E), the Health On the Net Code of Conduct (HONcode) and health trust indicator Web browser extensions.
This paper empirically assesses how financial risk aversion reacts to a change in individuals’ wealth and health and to the introduction of both financial and health risks using the Survey of Health, Ageing, and Retirement in Europe (SHARE). Individuals in our sample exhibit financial risk aversion decreasing both in wealth and health. Financial risk aversion is also found to increase in the presence of both background financial and health risks. Interestingly, the sensitivity of financial risk aversion to wealth, respectively to health, is shown to depend on the presence of a financial background risk, respectively health background risk, but in opposite directions. Such findings can help to better understand various economic decisions in a risky environment.
During the last three decades several hundred papers have been published on the broad topic of “program comprehension”. The goal was always the same: to develop models and tools to help developers with program understanding during program maintenance. However few authors targeted the more fundamental question: “what is program understanding” or, other words, proposed a model of program understanding. Then we reviewed the proposed program understanding models. We found the papers to be classifiable in three period of time in accordance with the following three subtopics: the process, the tools and the goals. Interestingly, studying the fundamental goal came after the tools. We conclude by highlighting that it is required to go back to the fundamental question to have any chance to develop effective tools to help with program understanding which is the most costly part of program maintenance.
Marketing instructors have traditionally sought to use experiential and active learning methods in their teaching. The flipped classroom is a learner-centered innovative pedagogical approach that moves the delivery of class material outside the classroom to focus on collaborative activities during class sessions. This qualitative exploratory research aims at understanding how students perceive their experience and the outcome of flipped classroom marketing courses in two Swiss universities. The analysis shows mixed results depending on the student population involved, as well as on the format of the preparatory material provided.
City-Zen is an interactive temporal knowledge-browsing platform that aims to valorise cultural heritages. While many organizations propose relevant data sets, they are hardly accessed, analysed and reused because of the formats inconsistency and the inappropriate information browsing and visualization. The goal of the project is to valorise the existing cultural heritage through a citizen centric design platform. The use case of this project involves a user willing to discover the history of a region and to embark in a cultural journey in the past. This project addresses a novel approach of data analysis of both assessment methods of data quality and spatio-temporal information. The proposed paper will describe the approach, its model and its implementation tests in the context of the State of Wallis.
Algorithms, data, services seem to create a net of semantic stability for users to consume information but come with concrete disadvantages regarding the way we understand, discuss and teach them. Social bookmarking applications are no exception to this, as they follow a similarly opaque way to organize and publish data. This article will examine the possibility to shift from predetermined results to open and descriptive protocols and applications that revisits fundamental web user activities such as search, classification, group formation and valorization of participation. This approach combines both a data handling protocol (CoWaBoo) and an application (collective observatories) that serve as wider concept and practice of application use and development. Our initial results from 2015 and 2016 university group course works, contributes to shifting our attention from how things end up, to how things become, an important competence in the field of ICT training and education.
The Health On the Net’s Foundation (HON) Code of Conduct, HONcode, is the oldest and the most used ethical and trustworthy code for medical and health related information available on the Internet. Until recently, websites voluntarily applying for the HONcode seal were evaluated manually by an expert medical team according to 8 principles, referred to as criteria, and associated published guidelines. In the scope of the European project Kconnect, HON is developing an automated system to identify the 8 HONcode criteria within health webpages. When the research on the development of such a system evolved from simple algorithmic testing to a real full-content setting, it revealed a number of issues. The preceding study consisted in taking a set of 27 health-related websites and having them assessed for their compliance to each of the 8 HONcode criterion, first manually by senior HONcode experts, and then through supervised machine learning by the automated system. The results showed disc repancies mainly for two criteria: “submerged content” under the Complementarity criterion and “extremely low recall” under the Date Attribution criterion. In this article, the authors investigate different approaches to solve the problems related to each of these criteria, namely a customized Named Entity Recognition Model instead of a machine learning component for Date Attribution, and a sliding window instead of the whole document as a unit of detection for Complementarity. The results obtained show that the newly adapted automated system greatly improves accuracy: 74% vs. 41% for the Date Attribution criterion and 74% vs. 22% for the Complementarity criterion.
