Credibility of Health Information and Digital Media: New Perspectives and Implications for Youth

Part of the Volume on Digital Media, Youth, and Credibility. This chapter considers the role of Web technologies on the availability and consumption of health information. It argues that young people are largely unfamiliar with trusted health sources online, making credibility particularly germane when considering this type of information. The author suggests that networked digital media allow for humans and technologies act as "apomediaries" that can be used to steer consumers to high quality health information, thereby empowering health information seekers of all ages

Factors Affecting the Behavioral Intention to Use Standalone Electronic Personal Health Record Applications by Adults in Egypt

Standalone electronic personal health record can be a useful tool that enables individuals to store, arrange and share their health information easily and they can build a history of their health timeline which is crucial for raising healthcare quality and better self-management, the adoption rate of these applications has been identified in several countries to be low and slowly progressing.Although there are some applications of standalone ePHR available in the market for usage free of charge but it’s almost not adopted at all, this study will investigate some of the factors that might affect the adoption of ePHR technology by adults in Egypt and provide business professionals a better picture for what can motivate or hinder the adoption process to achieve better adoption rates and eliminate the barriers.In order to ensure a comprehensive contextual analysis, researchers analyzed the research in hand with the perspective of the proposed contextual framework, the Nine Elements Framework/Model (Elsafty, 2018) that analyzes social studies research in general, and business/management reseaerches as well.Using the nine elements framework, the authors used it to discover the underlying factors that are causing the problems faced by the research in hand, and resulted in the coming contextual analysis defining the research scope and focus, which in the case of this paper is on Perceived usefulness and perceived ease of use were adapted from TAM that was initially developed by Fred Davis (1989) and they proved to have a high predictive power of behavioral intention in CHI context, The extensions of TAM including UTAUT & UTAUT2 seems to be irrelevant to this research context since UTAUT is more oriented towards the organizational context (Venkatesh et al., 2012) and UTAUT2 added factors, Price value seems to be irrelevant in this research context as we are already studying platforms that are provided free of charge, Hedonic motivation maybe irrelevant to this context as healthcare related service is mostly associated with seriousness and urgency, also testing unimplemented platforms that are not yet adopted makes from the habit unrealistic experience that may be inaccurate to measure.Since other several researches recommended extending these factors with other additional factors to make it more relevant to the healthcare consumer context (Kim & Park, 2012), these factors may include health-related factors, technology-related factors and personal-related factors. Findings in this research revealed that adoption rate in Egypt is still very low and high demand for this service which makes this research is significant as it’s trying to find out the reasons behind this gap, perceived usefulness, perceived ease of use, (privacy and security), eHealth literacy, personalization and awareness had a significant impact on behavioral intention to use standalone ePHR applications. Personalization was found to have the strongest effect on behavioral intention followed by perceived usefulness. Health status was found to have an insignificant effect on behavioral intention which indicates the interest of people with different health statuses in standalone ePHR

On the Presence of Green and Sustainable Software Engineering in Higher Education Curricula

Nowadays, software is pervasive in our everyday lives. Its sustainability and environmental impact have become major factors to be considered in the development of software systems. Millennials-the newer generation of university students-are particularly keen to learn about and contribute to a more sustainable and green society. The need for training on green and sustainable topics in software engineering has been reflected in a number of recent studies. The goal of this paper is to get a first understanding of what is the current state of teaching sustainability in the software engineering community, what are the motivations behind the current state of teaching, and what can be done to improve it. To this end, we report the findings from a targeted survey of 33 academics on the presence of green and sustainable software engineering in higher education. The major findings from the collected data suggest that sustainability is under-represented in the curricula, while the current focus of teaching is on energy efficiency delivered through a fact-based approach. The reasons vary from lack of awareness, teaching material and suitable technologies, to the high effort required to teach sustainability. Finally, we provide recommendations for educators willing to teach sustainability in software engineering that can help to suit millennial students needs.Comment: The paper will be presented at the 1st International Workshop on Software Engineering Curricula for Millennials (SECM2017

How Can Organizations Design Purposeful Human-AI Interactions: A Practical Perspective From Existing Use Cases and Interviews

Artificial intelligence (AI) currently makes a tangible impact in many industries and humans’ daily lives. With humans interacting with AI agents more regularly, there is a need to examine human-AI interactions to design them purposefully. Thus, we draw on existing AI use cases and perceptions of human-AI interactions from 25 interviews with practitioners to elaborate on these interactions. From this practical lens on existing human-AI interactions, we introduce nine characteristic dimensions to describe human-AI interactions and distinguish five interaction types according to AI agents’ characteristics in the human-AI interaction. Besides, we provide initial design guidelines to stimulate both research and practice in creating purposeful designs for human-AI interactions

The Acceptability and Usability of Digital Health Interventions for Adults With Depression, Anxiety, and Somatoform Disorders: Qualitative Systematic Review and Meta-Synthesis

The prevalence of mental health disorders continues to rise, with almost 4% of the world population having an anxiety disorder and almost 3.5% having depression in 2017. Despite the high prevalence, only one-third of people with depression or anxiety receive treatment. Over the last decade, the use of digital health interventions (DHIs) has risen rapidly as a means of accessing mental health care and continues to increase. Although there is evidence supporting the effectiveness of DHIs for the treatment of mental health conditions, little is known about what aspects are valued by users and how they might be improved. This systematic review aimed to identify, appraise, and synthesize the qualitative literature available on service users' views and experiences regarding the acceptability and usability of DHIs for depression, anxiety, and somatoform disorders. A systematic search strategy was developed, and searches were run in 7 electronic databases. Qualitative and mixed methods studies published in English were included. A meta-synthesis was used to interpret and synthesize the findings from the included studies. A total of 24 studies were included in the meta-synthesis, and 3 key themes emerged with descriptive subthemes. The 3 key themes were initial motivations and approaches to DHIs, personalization of treatment, and the value of receiving personal support in DHIs. The meta-synthesis suggests that participants' initial beliefs about DHIs can have an important effect on their engagement with these types of interventions. Personal support was valued very highly as a major component of the success of DHIs. The main reason for this was the way it enabled individual personalization of care. Findings from the systematic review have implications for the design of future DHIs to improve uptake, retention, and outcomes in DHIs for depression, anxiety, and somatoform disorders. DHIs need to be personalized to the specific needs of the individual. Future research should explore whether the findings could be generalized to other health conditions. [Abstract copyright: ©Shireen Patel, Athfah Akhtar, Sam Malins, Nicola Wright, Emma Rowley, Emma Young, Stephanie Sampson, Richard Morriss. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 06.07.2020.

The relevance internet users assign to algorithmic-selection applications in everyday life

The rapidly growing academic and public attention to algorithmic-selection applications such as search engines and social media is indicative of their alleged great social relevance and impact on daily life in digital societies. To substantiate these claims, this paper investigates the hitherto little explored subjective relevance that Internet users assign to algorithmic-selection applications in everyday life. A representative online survey of Internet users comparatively reveals the relevance that users ascribe to algorithmic-selection applications and to their online and offline alternatives in five selected life domains: political and social orientation, entertainment, commercial transactions, socializing and health. The results show that people assign a relatively low relevance to algorithmic-selection applications compared to offline alternatives across the five life domains. The findings vary greatly by age and education. Altogether, such outcomes complement and qualify assessments of the social impact of algorithms that are primarily and often solely based on usage data and theoretical considerations

DESIGNING FAIR AI SYSTEMS: HOW EXPLANATION SPECIFICITY INFLUENCES USERS’ PERCEIVED FAIRNESS AND TRUSTING INTENTIONS

Artificial intelligence (AI) is revolutionizing the way we make decisions, but it is rarely perfect, and human-centric AI calls for a thorough empirical understanding of how the the theoretical fairness notion translates into perceptions of fairness in practical scenarios. Drawing upon the explainable artificial intelligence literature and elaboration likelihood model, we investigate the interaction effects of explanation specificity of AIs and issue involvement of users. We used a 3x2 experiment design with 456 participants to verify the proposed research model. We found that for individuals of low issue involvement, AI with global explanation is more effective, while AI feature-based explanation is more effective in influencing high issues involved individuals on their fairness perceptions of AI decisions, consequently leading to their trusting intentions towards AI decision-making systems. This study significantly contributes to the theoretical landscape of AI fairness and human-AI interaction, and provide important practical contributions for AI designers