Explore the vast range of titles available.

In the field of risks and crises, both the access to relevant information and its circulation are seen as crucial factors. Based on a new integrated theoretical model focusing on the stakeholder, the book proposes analysis of information reformulation and circulation in risk environments and crisis situations. Simply circulating the information does not mean that it will be picked up by those who could benefit from it. This has been amply demonstrated by the various crises and catastrophes that have shaken the planet in recent years. In order to be able to deal with risk situations and crises, it must be possible for information ? when it circulates ? to be understood and interpreted by a wide range of stakeholders, working in fields such as health and natural or environmental risks. By observing closely, in three very different situations, the way in which information is gathered, processed, distributed and used, this book examines the countless reformulations, redefinitions and even reorientations to which all information is subjected. This multiple reformatting, at least according to the hypothesis put forward in this book, is an important element in ensuring that the information produced circulates and reaches those for whom it is intended. The intention is then to analyze the way in which information circulates in situations of risk and crisis. In order to do it, the authors propose a new theoretical model based on different approaches. This model is anchored in the trend of research that has been oriented towards a wider understanding of risks and their territorial and social consequences. These ideas question the approach to risk which focuses primarily on technical aspects and probability. The model also draws from approaches to risk that focus on the stakeholders involved in the debates and the need for an integrated vision of risks. Risks are thus considered heterogeneous, plural and transcalar. The information flow about risks was studied first in the SHOC Room of the World Health Organization (WHO) in Geneva, a central place through which passes all information destined to managing world-wide epidemic risks. Then the research team monitored the constitution and the reception of a field library about risks management and reduction sent to Madagascar, an island systematically hit by cyclones. This following process has permitted the analysis of information dissemination during a crisis situation. The third field work was done in Cameroun to observe the use and transmission of information in two NGO specializing in sanitary risks prevention using traditional and biomedical conceptualization of health and illness. The book ends with a practical tool to assess and help the information circulation in risk and crisis situations.

With online health information becoming increasingly popular among patients, concerns have been raised about the impact of patients' Internet health information-seeking behavior on their relationship with physicians. Therefore, it is pertinent to understand the influence of online health information on the patient-physician relationship. Our objective was to systematically review existing research on patients' Internet health information seeking and its influence on the patient-physician relationship. We systematically searched PubMed and key medical informatics, information systems, and communication science journals covering the period of 2000 to 2015. Empirical articles that were in English were included. We analyzed the content covering themes in 2 broad categories: factors affecting patients' discussion of online findings during consultations and implications for the patient-physician relationship. We identified 18 articles that met the inclusion criteria and the quality requirement for the review. The articles revealed barriers, facilitators, and demographic factors that influence patients' disclosure of online health information during consultations and the different mechanisms patients use to reveal these findings. Our review also showed the mechanisms in which online information could influence patients' relationship with their physicians. Results of this review contribute to the understanding of the patient-physician relationship of Internet-informed patients. Our main findings show that Internet health information seeking can improve the patient-physician relationship depending on whether the patient discusses the information with the physician and on their prior relationship. As patients have better access to health information through the Internet and expect to be more engaged in health decision making, traditional models of the patient-provider relationship and communication strategies must be revisited to adapt to this changing demographic.

With the world's population aging, more health-conscious older adults are seeking health information to make better-informed health decisions. The rapid growth of the internet has empowered older adults to access web-based health information sources. However, research explicitly exploring older adults' online health information seeking (OHIS) behavior is still underway. This systematic scoping review aims to understand older adults' OHIS and answer four research questions: (1) What types of health information do older adults seek and where do they seek health information on the internet? (2) What are the factors that influence older adults' OHIS? (3) What are the barriers to older adults' OHIS? (4) How can we intervene and support older adults' OHIS? A comprehensive literature search was performed in November 2020, involving the following academic databases: Web of Science; Cochrane Library database; PubMed; MEDLINE; CINAHL Plus; APA PsycINFO; Library and Information Science Source; Library, Information Science and Technology Abstracts; Psychology and Behavioral Sciences Collection; Communication & Mass Media Complete; ABI/INFORM; and ACM Digital Library. The initial search identified 8047 publications through database search strategies. After the removal of duplicates, a data set consisting of 5949 publications was obtained for screening. Among these, 75 articles met the inclusion criteria. Qualitative content analysis was performed to identify themes related to the research questions. The results suggest that older adults seek 10 types of health information from 6 types of internet-based information sources and that 2 main categories of influencing factors, individual-related and source-related, impact older adults' OHIS. Moreover, the results reveal that in their OHIS, older adults confront 3 types of barriers, namely individual, social, and those related to information and communication technologies. Some intervention programs based on educational training workshops have been created to intervene and support older adults' OHIS. Although OHIS has become increasingly common among older adults, the review reveals that older adults' OHIS behavior is not adequately investigated. The findings suggest that more studies are needed to understand older adults' OHIS behaviors and better support their medical and health decisions in OHIS. Based on the results, the review proposes multiple objectives for future studies, including (1) more investigations on the OHIS behavior of older adults above 85 years; (2) conducting more longitudinal, action research, and mixed methods studies; (3) elaboration of the mobile context and cross-platform scenario of older adults' OHIS; (4) facilitating older adults' OHIS by explicating technology affordance; and (5) promoting and measuring the performance of OHIS interventions for older adults.

As large language model (LLM)-based chatbots such as ChatGPT (OpenAI) grow in popularity, it is essential to understand their role in delivering online health information compared to other resources. These chatbots often generate inaccurate content, posing potential safety risks. This motivates the need to examine how users perceive and act on health information provided by LLM-based chatbots. This study investigates the patterns, perceptions, and actions of users seeking health information online, including LLM-based chatbots. The relationships between online health information-seeking behaviors and important sociodemographic characteristics are examined as well. A web-based survey of crowd workers was conducted via Prolific. The questionnaire covered sociodemographic information, trust in health care providers, eHealth literacy, artificial intelligence (AI) attitudes, chronic health condition status, online health information source types, perceptions, and actions, such as cross-checking or adherence. Quantitative and qualitative analyses were applied. Most participants consulted search engines (291/297, 98%) and health-related websites (203/297, 68.4%) for their health information, while 21.2% (63/297) used LLM-based chatbots, with ChatGPT and Microsoft Copilot being the most popular. Most participants (268/297, 90.2%) sought information on health conditions, with fewer seeking advice on medication (179/297, 60.3%), treatments (137/297, 46.1%), and self-diagnosis (62/297, 23.2%). Perceived information quality and trust varied little across source types. The preferred source for validating information from the internet was consulting health care professionals (40/132, 30.3%), while only a very small percentage of participants (5/214, 2.3%) consulted AI tools to cross-check information from search engines and health-related websites. For information obtained from LLM-based chatbots, 19.4% (12/63) of participants cross-checked the information, while 48.4% (30/63) of participants followed the advice. Both of these rates were lower than information from search engines, health-related websites, forums, or social media. Furthermore, use of LLM-based chatbots for health information was negatively correlated with age (ρ=-0.16, P=.006). In contrast, attitudes surrounding AI for medicine had significant positive correlations with the number of source types consulted for health advice (ρ=0.14, P=.01), use of LLM-based chatbots for health information (ρ=0.31, P<.001), and number of health topics searched (ρ=0.19, P<.001). Although traditional online sources remain dominant, LLM-based chatbots are emerging as a resource for health information for some users, specifically those who are younger and have a higher trust in AI. The perceived quality and trustworthiness of health information varied little across source types. However, the adherence to health information from LLM-based chatbots seemed more cautious compared to search engines or health-related websites. As LLMs continue to evolve, enhancing their accuracy and transparency will be essential in mitigating any potential risks by supporting responsible information-seeking while maximizing the potential of AI in health contexts.

The abundance of health information available online provides consumers with greater access to information pertinent to the management of health conditions. This is particularly important given an increasing drive for consumer-focused health care models globally, especially in the management of chronic health conditions, and in recognition of challenges faced by lay consumers with finding, understanding, and acting on health information sourced online. There is a paucity of literature exploring the navigational needs of consumers with regards to accessing online health information. Further, existing interventions appear to be didactic in nature, and it is unclear whether such interventions appeal to consumers' needs. Our goal was to explore the navigational needs of consumers with chronic health conditions in finding online health information within the broader context of consumers' online health information-seeking behaviors. Potential barriers to online navigation were also identified. Semistructured interviews were conducted with adult consumers who reported using the Internet for health information and had at least one chronic health condition. Participants were recruited from nine metropolitan community pharmacies within Western Australia, as well as through various media channels. Interviews were audio-recorded, transcribed verbatim, and then imported into QSR NVivo 10. Two established approaches to thematic analysis were adopted. First, a data-driven approach was used to minimize potential bias in analysis and improve construct and criterion validity. A theory-driven approach was subsequently used to confirm themes identified by the former approach and to ensure identified themes were relevant to the objectives. Two levels of analysis were conducted for both data-driven and theory-driven approaches: manifest-level analysis, whereby face-value themes were identified, and latent-level analysis, whereby underlying concepts were identified. We conducted 17 interviews, with data saturation achieved by the 14th interview. While we identified a broad range of online health information-seeking behaviors, most related to information discussed during consumer-health professional consultations such as looking for information about medication side effects. The barriers we identified included intrinsic barriers, such as limited eHealth literacy, and extrinsic barriers, such as the inconsistency of information between different online sources. The navigational needs of our participants were extrinsic in nature and included health professionals directing consumers to appropriate online resources and better filtering of online health information. Our participants' online health information-seeking behaviors, reported barriers, and navigational needs were underpinned by the themes of trust, patient activation, and relevance. This study suggests that existing interventions aimed to assist consumers with navigating online health information may not be what consumers want or perceive they need. eHealth literacy and patient activation appear to be prevalent concepts in the context of consumers' online health information-seeking behaviors. Furthermore, the role for health professionals in guiding consumers to quality online health information is highlighted.

Previous studies have suggested that patients' online health information seeking affects their medical consultations and patient-doctor relationships. An up-to-date picture of patients' online health information-seeking behaviors can inform and prepare frontline health care professionals to collaborate, facilitate, or empower their patients to access and manage health information found online. This study explores the prevalence, patterns, and predictors of online health information-seeking behaviors among primary care patients in Hong Kong, and the relations between online health information seeking and electronic health (eHealth) literacy. Patients attending a university primary care clinic in Hong Kong were asked to complete a questionnaire survey on their demographic backgrounds; health status; frequency and pattern of online health information seeking; contents, sources, and reasons for online health information seeking; and their eHealth literacy. eHealth literacy was measured by the validated eHealth Literacy Scale (eHEALS). Regression analyses explored various demographic and behavioral predictors to online health information seeking, and predictors to eHealth literacy. In all, 97.32% (1162/1194) respondents used the internet, of which 87.44% (1016/1162) had used the internet to find health information. Most respondents (65.97%, 665/1008) searched once monthly or more. Few (26.88%, 271/1008) asked their doctor about health information found online, but most doctors (56.1%, 152/271) showed little or no interest at all. The most sought topic was symptom (81.59%, 829/1016), the top reason was noticing new symptoms or change in health (70.08%, 712/1016), the most popular source was online encyclopedia (69.98%, 711/1016), and the top reason for choosing a source was convenience (55.41%, 563/1016). Poisson regression analysis identified high eHEALS score, fair or poor self-rated health, having a chronic medical condition, and using the internet several times a day as significant predictors of online health information seeking. Multiple regression analysis identified lower age, better self-rated health, more frequent internet use, more frequent online health information seeking, and more types of health information sought as significant predictors to higher eHealth literacy. Online health information seeking is prevalent among primary care patients in Hong Kong, but only a minority shared the information with doctors. Websites were chosen more for convenience than for accuracy or authoritativeness. Doctors should recognize patients' online health information-seeking behavior, and facilitate and empower them to search for high-quality online health information.

The COVID-19 pandemic has impacted people's lives at unprecedented speed and scale, including how they eat and work, what they are concerned about, how much they move, and how much they can earn. Traditional surveys in the area of public health can be expensive and time-consuming, and they can rapidly become outdated. The analysis of big data sets (such as electronic patient records and surveillance systems) is very complex. Google Trends is an alternative approach that has been used in the past to analyze health behaviors; however, most existing studies on COVID-19 using these data examine a single issue or a limited geographic area. This paper explores Google Trends as a proxy for what people are thinking, needing, and planning in real time across the United States. We aimed to use Google Trends to provide both insights into and potential indicators of important changes in information-seeking patterns during pandemics such as COVID-19. We asked four questions: (1) How has information seeking changed over time? (2) How does information seeking vary between regions and states? (3) Do states have particular and distinct patterns in information seeking? (4) Do search data correlate with-or precede-real-life events? We analyzed searches on 38 terms related to COVID-19, falling into six themes: social and travel; care seeking; government programs; health programs; news and influence; and outlook and concerns. We generated data sets at the national level (covering January 1, 2016, to April 15, 2020) and state level (covering January 1 to April 15, 2020). Methods used include trend analysis of US search data; geographic analyses of the differences in search popularity across US states from March 1 to April 15, 2020; and principal component analysis to extract search patterns across states. The data showed high demand for information, corresponding with increasing searches for coronavirus linked to news sources regardless of the ideological leaning of the news source. Changes in information seeking often occurred well in advance of action by the federal government. The popularity of searches for unemployment claims predicted the actual spike in weekly claims. The increase in searches for information on COVID-19 care was paralleled by a decrease in searches related to other health behaviors, such as urgent care, doctor's appointments, health insurance, Medicare, and Medicaid. Finally, concerns varied across the country; some search terms were more popular in some regions than in others. COVID-19 is unlikely to be the last pandemic faced by the United States. Our research holds important lessons for both state and federal governments in a fast-evolving situation that requires a finger on the pulse of public sentiment. We suggest strategic shifts for policy makers to improve the precision and effectiveness of non-pharmaceutical interventions and recommend the development of a real-time dashboard as a decision-making tool.

Adaptive information seeking is critical for goal-directed behavior. Growing evidence suggests the importance of intrinsic motives such as curiosity or need for novelty, mediated through dopaminergic valuation systems, in driving information-seeking behavior. However, valuing information for its own sake can be highly suboptimal when agents need to evaluate instrumental benefit of information in a forward-looking manner. Here we show that information-seeking behavior in humans is driven by subjective value that is shaped by both instrumental and noninstrumental motives, and that this subjective value of information (SVOI) shares a common neural code with more basic reward value. Specifically, using a task where subjects could purchase information to reduce uncertainty about outcomes of a monetary lottery, we found information purchase decisions could be captured by a computational model of SVOI incorporating utility of anticipation, a form of noninstrumental motive for information seeking, in addition to instrumental benefits. Neurally, trial-by-trial variation in SVOI was correlated with activity in striatum and ventromedial prefrontal cortex. Furthermore, cross-categorical decoding revealed that, within these regions, SVOI and expected utility of lotteries were represented using a common code. These findings provide support for the common currency hypothesis and shed insight on neurocognitive mechanisms underlying information-seeking behavior.

Generation Z (born 1995-2010) members are digital residents who use technology and the internet more frequently than any previous generation to learn about their health. They are increasingly moving away from conventional methods of seeking health information as technology advances quickly and becomes more widely available, resulting in a more digitalized health care system. Similar to all groups, Generation Z has specific health care requirements and preferences, and their use of technology influences how they look for health information. However, they have often been overlooked in scholarly research. First, we aimed to identify the information-seeking preferences of older individuals and Generation Z (those between the ages of 18 and 26 years); second, we aimed to predict the effects of digital health literacy and health empowerment in both groups. We also aimed to identify factors that impact how both groups engage in digital health and remain in control of their own health. The Health Information National Trends Survey was adopted for further use in 2022. We analyzed 1862 valid data points by conducting a survey among Chinese respondents to address the research gap. A descriptive analysis, 2-tailed t test, and multiple linear regression were applied to the results. When compared with previous generations, Generation Z respondents (995/1862, 53.44%) were more likely to use the internet to find out about health-related topics, whereas earlier generations relied more on traditional media and interpersonal contact. Web-based information-seeking behavior is predicted by digital health literacy (Generation Z: β=.192, P<.001; older population: β=.337, P<.001). While this was happening, only seeking health information from physicians positively predicted health empowerment (Generation Z: β=.070, P=.002; older population: β=.089, P<.001). Despite more frequent use of the internet to learn about their health, Generation Z showed lower levels of health empowerment and less desire to look for health information, overall. This study examined and compared the health information–seeking behaviors of Generation Z and older individuals to improve their digital health literacy and health empowerment. The 2 groups demonstrated distinct preferences regarding their choice of information sources. Health empowerment and digital health literacy were both significantly related to information-seeking behaviors.