Disaster Continuity for Businesses and Communities in Rural Texas: Investigating Infrastructure, Communication, and Planning Needs

Small businesses are particularly vulnerable to weather shocks. While there is a plethora of publicly available resources on how to prepare for natural disasters, information tailored specifically for small business owners and their business operations and assets is more limited. This project studied the most disaster-vulnerable areas in Texas, particularly the Texas Gulf Coast’s Coastal Bend and Rio Grande Valley regions, with the goal of making small businesses and communities along the coast more disaster resilient. A primary goal of this project was to develop resources for small businesses in the Lower Rio Grande Valley (LRGV). They were developed with extensive community input, and the local Chambers of Commerce and the Agrilife Extension Agents have agreed to distribute these materials.IC2 Institut

Design simplicity influences patient portal use: the role of aesthetic evaluations for technology acceptance

Objective This study focused on patient portal use and investigated whether aesthetic evaluations of patient portals function are antecedent variables to variables in the Technology Acceptance Model

Alert Networks of ICTs and Sources in Campus Emergencies

ABSTRACT This study contributes an understanding of how ICTs and varying information sources work together during emergency alerts. It builds on the prior work on campus active shooter events by examining an organization that used a range of ICTs including mobile devices, social media, organizational tools, and news media, to notify their stakeholders about an emergency. The study design used a survey to capture the responses from a random sample of over 1000 stakeholders-students, faculty, and staff-who were notified of an active shooter emergency. The findings from the first three notifications suggest that messages reaching the most stakeholders were (a) sent by official sources through ICTs like mobile phones; (b) official email communication, and (c) messages that included face-to-face communication. While 11 different ICTs were included in the study, mass media (i.e., television and radio), and social media (Twitter and Facebook) did not function substantially in the emergency alert process

Alert Networks of ICTs and Sources in Campus Emergencies

ABSTRACT This study contributes an understanding of how ICTs and varying information sources work together during emergency alerts. It builds on the prior work on campus active shooter events by examining an organization that used a range of ICTs including mobile devices, social media, organizational tools, and news media, to notify their stakeholders about an emergency. The study design used a survey to capture the responses from a random sample of over 1000 stakeholders-students, faculty, and staff-who were notified of an active shooter emergency. The findings from the first three notifications suggest that messages reaching the most stakeholders were (a) sent by official sources through ICTs like mobile phones; (b) official email communication, and (c) messages that included face-to-face communication. While 11 different ICTs were included in the study, mass media (i.e., television and radio), and social media (Twitter and Facebook) did not function substantially in the emergency alert process

ASIS&T Webinar and Discussion: The Role of Information During a Global Health Crisis - Association for Information Science and Technology

You're viewing a past blog from the Good Systems Grand Challenge team at The University of Texas at Austin about free webinars offered to discuss the current and future effects of global crisis.Office of the VP for Researc

Human-AI Teaming During an Ongoing Disaster: How Scripts Around Training and Feedback Reveal this is a Form of Human-Machine Communication

Humans play an integral role in identifying important information from social media during disasters. While human annotation of social media data to train machine learning models is often viewed as human-computer interaction, this study interrogates the ontological boundary between such interaction and human-machine communication. We conducted multiple interviews with participants who both labeled data to train machine learning models and corrected machine-inferred data labels. Findings reveal three themes: scripts invoked to manage decision-making, contextual scripts, and scripts around perceptions of machines. Humans use scripts around training the machine—a form of behavioral anthropomorphism—to develop social relationships with them. Correcting machine-inferred data labels changes these scripts and evokes self-doubt around who is right, which substantiates the argument that this is a form of human-machine communication

Global health crises are also information crises: A call to action

Association for Information Science & Technology published a piece from Bo Xie and others about the Misinformation/disinformation particularly during global health crises on March 13, 2020.Office of the VP for Researc

Computational medicine, present and the future: obstetrics and gynecology perspective.

Medicine is, in its essence, decision making under uncertainty; the decisions are made about tests to be performed and treatments to be administered. Traditionally, the uncertainty in decision making was handled using expertise collected by individual providers and, more recently, systematic appraisal of research in the form of evidence-based medicine. The traditional approach has been used successfully in medicine for a very long time. However, it has substantial limitations because of the complexity of the system of the human body and healthcare. The complex systems are a network of highly coupled components intensely interacting with each other. These interactions give those systems redundancy and thus robustness to failure and, at the same time, equifinality, that is, many different causative pathways leading to the same outcome. The equifinality of the complex systems of the human body and healthcare system demand the individualization of medical care, medicine, and medical decision making. Computational models excel in modeling complex systems and, consequently, enabling individualization of medical decision making and medicine. Computational models are theory- or knowledge-based models, data-driven models, or models that combine both approaches. Data are essential, although to a different degree, for computational models to successfully represent complex systems. The individualized decision making, made possible by the computational modeling of complex systems, has the potential to revolutionize the entire spectrum of medicine from individual patient care to policymaking. This approach allows applying tests and treatments to individuals who receive a net benefit from them, for whom benefits outweigh the risk, rather than treating all individuals in a population because, on average, the population benefits. Thus, the computational modeling-enabled individualization of medical decision making has the potential to both improve health outcomes and decrease the costs of healthcare

Channel choice complications:Exploring the multiplex nature of citizens' channel choices

Part 2: E-Government Services and Open GovernmentInternational audienceIn spite of massive investment and increased adoption of digital services, citizens continue to use traditional channels to interact with public organizations. The channel choice (CC) field of research tries to understand citizens’ interactions with public authorities to make the interaction more efficient and increase citizen satisfaction. However, most studies have been conducted either as surveys of hypothetical services or in experimental settings, leading to a lack of empirical data from actual use contexts. Therefore, we present the results of a sequential mixed methods study which combines observations of citizen-caseworker interaction in a call center, contextual interviews with callers, and a survey classifying topics from 10,000 telephone calls. We contribute to the CC field and practice with rich empirical data from studies of actual channel choices. Specifically, the study explores the multiplex nature of real-life CC and demonstrate how telephone calls can be part of a process, which occurs across both traditional and digital channels. Moreover, we identify problems, which cause telephone calls related to digital services, and classify these in two groups: information related problems and action related problems