Information avoidance in the age of COVID-19: A meta-analysis

Reasons people avoid colorectal cancer information: a mixed-methods study

OBJECTIVE

With screening, colorectal cancer can be detected when treatable, or even prevented. However, approximately one in five people tend to avoid colorectal cancer information, and avoidance is associated with being less likely to have been screened for the disease. Crucial to developing strategies to reduce information avoidance, we sought a comprehensive understanding of reasons people avoid colorectal cancer information.

METHODS AND MEASURES

In a mixed methods study, we surveyed 200 participants who varied with respect to avoidance and interviewed 15 people who tended to avoid colorectal cancer information (all aged 40-75) about reasons for avoiding.

RESULTS

In both survey and interviews, primary reasons for information avoidance were: (1) shielding from anxiety and other aversive emotion, (2) perceived information sufficiency and (3) feelings of information overload. Trait anxiety, fear of diagnosis, anticipating negative interactions with healthcare, and negative associations with screening procedures exacerbated avoidance. Participants justified information non-relevance by attributing risk to other people's characteristics such as family history, gastrointestinal symptoms, being male, or living an unhealthy lifestyle.

CONCLUSION

Novel findings include the triggering influence of trait anxiety and financial constraints on information avoidance. Also, information overload and incorrect understanding of risk factors may exacerbate perceptions of information sufficiency and avoidance.

A Meta-Analysis of Artificial Intelligence Technologies Use and Loneliness: Examining the Influence of Physical Embodiment, Age Differences, and Effect Direction

Recent research has investigated the connection between artificial intelligence (AI) utilization and feelings of loneliness, yielding inconsistent outcomes. This meta-analysis aims to clarify this relationship by synthesizing data from 47 relevant studies across 21 publications. Findings indicate a generally significant positive correlation between AI use and loneliness (r = 0.163, p < 0.05). Specifically, interactions with physically embodied AI are marginally significantly associated with decreased loneliness (r = -0.266, p = 0.088), whereas engagement with physically disembodied AI is significantly linked to increased loneliness (r = 0.352, p < 0.001). Among older adults (aged 60 and above), AI use is significantly positively associated with loneliness (r = 0.352, p < 0.001), while no significant correlation is observed (r = 0.039, p = 0.659) in younger individuals (aged 35 and below). Furthermore, by incorporating positive attitudes toward AI, the study reveals that the influence of AI use in exacerbating loneliness outweighs the reverse impact, although both directions show significant positive relationships. These results enhance the understanding of how AI usage relates to loneliness and provide practical insights for addressing loneliness through AI technologies.

COVID-19 Misperceptions and Masking Compliance: A Support Marshaling Analysis

Misperceptions strongly influence the extent to which individuals comply with preventative measures. Social support from others, particularly given widespread mistrust in news media among those holding misperceptions, plays an important role in shaping compliance with preventative measures. The impact of social support, however, is not straightforward and not all support results in greater compliance. The goal of this study is to examine the role of COVID-19 misperceptions in shaping support marshaling and its associations with emotions about masking as well as compliance with masking measures. The findings broadly identify that those who engage avoidance support marshaling are likely fostering echo-chambers, reinforcing misperception and emotions about masking that limit their willingness to comply. Alternatively, those who are approaching support are likely encountering diverse opinions and increasing the opportunity to discuss misperception that influences emotions in ways that may foster more compliance. Implications of these findings for theory and methodological development are discussed.

A model for predicting factors affecting health information avoidance on WeChat

Objective

WeChat serves as a crucial source of health information, distinguished by its highly personalized nature. Avoidance of such personalized health information has a direct impact on individuals' health decision-making. This study aims to identify the factors influencing personalized health information avoidance on WeChat and to construct a hierarchical framework illustrating the relationships among these factors.

Methods

A hybrid method was utilized. Semi-structured interviews and grounded theory were used to identify the influencing factors. The interpretive structural modeling (ISM) method was adopted to develop a hierarchical model of the identified factors, followed by matrice d'impacts croises-multiplication appliqué a un classemen (MICMAC) to analyze the dependence and driving power of each factor.

Results

The 20 predictors of personalized health information avoidance were broadly categorized into three groups: personal, informational, and social factors. These factors collectively form a three-tier explanatory framework, consisting of the top, middle and bottom layers. At the root layer, health characteristics and cognition exerted a strong driving force, while negative emotions and affective factors at the top layer showed a high degree of dependence. In contrast, the decision-making cognition, informational factors, and social factors in the middle layer exhibited relatively weaker driving force and dependence power.

Conclusion

This study bridged the research gap of information avoidance by providing new insights targeting the factors influencing personalized health information avoidance behavior on WeChat. It also contributed to enhancing personal health information management and the health information services provided on WeChat.

Jean B, Behre J, Raymond K, Liu BF, Shi D. “We Don't Have a Lot of Answers”: An Investigation of COVID Long‐Haulers' Information Needs & Practices

The information behaviors of people who have COVID‐19 have been well studied, however, there is a gap regarding those who experience long COVID. We conducted an exploratory mixed‐methods study to examine the information needs and practices of these COVID ‘long‐haulers’ with the intention of suggesting new health communication strategies to help people in similar health crises or future pandemics from an information standpoint. Several themes have emerged from our preliminary findings: how participants tend to define long COVID, their long COVID‐related information seeking and their difficulty with finding such information, and their experiences with discrimination and stigma. Throughout data collection, we have experienced issues with suspicious respondents (such as potential bots), though we were able to implement new criteria to determine legitimacy. Data collection and analysis is still ongoing and we expect to report additional findings in the near future.

Generation Z's Health Information Avoidance Behavior: Insights From Focus Group Discussions

BACKGROUND

Younger generations actively use social media to access health information. However, research shows that they also avoid obtaining health information online at times when confronted with uncertainty.

OBJECTIVE

This study aims to examine the phenomenon of health information avoidance among Generation Z, a representative cohort of active web users in this era.

METHODS

Drawing on the planned risk information avoidance model, we adopted a qualitative approach to explore the factors related to information avoidance within the context of health and risk communication. The researchers recruited 38 participants aged 16 to 25 years for the focus group discussion sessions.

RESULTS

In this study, we sought to perform a deductive qualitative analysis of the focus group interview content with open, focused, and theoretical coding. Our findings support several key components of the planned risk information avoidance model while highlighting the underlying influence of cognition on emotions. Specifically, socioculturally, group identity and social norms among peers lead some to avoid health information. Cognitively, mixed levels of risk perception, conflicting values, information overload, and low credibility of information sources elicited their information avoidance behaviors. Affectively, negative emotions such as anxiety, frustration, and the desire to stay positive contributed to avoidance.

CONCLUSIONS

This study has implications for understanding young users' information avoidance behaviors in both academia and practice.

Predictors of adherence in Austrian employees during the COVID-19 pandemic: results of an online survey

Background

Since the beginning of the pandemic in December 2019, Coronavirus disease 2019 (COVID-19) has been a significant challenge to health care systems throughout the world. The introduction of measures to reduce the incidence of infection had a significant impact on the workplace. Overall, companies played a key and adaptive role in coping with the pandemic.

Methods

Cross-sectional data from an online-survey of 1,183 employees conducted during the COVID-19 pandemic in spring 2021 in Austria were used in the analyses. The influence of health beliefs (e.g., perceived severity), modifying factors (e.g., age) and time-dependent factors (e.g., corona fatigue) on individual adherence were evaluated. The conception of the questionnaire was based on the health belief model.

Results

The majority of respondents were female (58.3%), worked in companies with more than 250 employees (56.6%) and had been to an academic secondary school or had a university degree (58.3%). Overall, employees were adherent to most of the measures at their company (>80%), except for wearing FFP-2 masks when they were travelling in a car with coworkers (59.3, 95%CI 51.3-66.7%). Overall adherence was associated with high ratings for the meaningfulness of testing (OR: 2.06 95%CI: 1.00-4.22; p = 0.049), the extent to which social norms govern behavior (OR: 6.61 95%CI: 4.66-9.36; p < 0.001), lower perceived difficulties associated with the adoption of health-promoting measures (OR: 0.37 95%CI: 0.16-0.82; p = 0.015) and lower corona fatigue (OR: 0.23 95%CI: 0.10-0.52; p < 0.001). Adherence to four single measures was influenced by different predictors. The most important predictors (important for the adherence to three out of four single measures) were social norms and corona fatigue.

Conclusion

The importance attached to testing and social norms, as well as lower perceived barriers to health-promoting measures and low levels of corona fatigue all increase overall adherence to Covid-19 protective measures in companies. Strategies to improve adherence should be adapted depending on the aim (to raise overall adherence or adherence to individual measures) and on the group of persons that is being targeted.

Health information avoidance and health promotion behavior in patients with enterostomy

BACKGROUND

Enterostomy is currently the main treatment method for colorectal cancer. Health promotion behavior can improve the quality of life of patients undergoing enterostomy and is of great significance in maintaining their health status. However, health information avoidance can drive patients to avoid health risk information, which is not conducive to their own health. This study hypothesized that health information avoidance in patients undergoing colostomy is the main factor influencing health promotion behavior.

AIM

To investigate the status of health information avoidance and health promoting behavior among enterostomy patients and discuss their relationship, in order to provide reference for improving the prognosis and quality of life of patients with enterostomy.

METHODS

By using the convenient sampling method, 205 enterostomy patients were selected from a hospital in Qingdao. General information questionnaire, Health Information Avoidance Scale, and Health Promoting Lifestyle Profile-Ⅱ were used to conduct the investigation.

RESULTS

The health information avoidance score of patients with enterostomy was (25.99 ± 8.81), and 105 patients (56.10%) had varying degrees of health information avoidance behavior, of whom 64 (31.20%) had mild avoidance and 41 (24.90%) had severe avoidance. The Health Promoting Lifestyle Profile-Ⅱ score was (126.19 ± 15.32), which was overall in the middle level. Health information avoidance was negatively correlated with health promotion behavior. Multiple linear regression analysis showed that health information avoidance behavior was a significant influencing factor of health promotion behavior.

CONCLUSION

The health information avoidance behavior and health promotion behavior of patients with enterostomy need to be improved. The medical staff should understand the obstacles of patients in the process of receiving health information, in order to help them effectively cope with the avoidance of health information and improve their health behavior and quality of life.

Infodemic: Challenges and solutions in topic discovery and data process

BACKGROUND

The Coronavirus Disease 2019 (COVID-19) pandemic was a huge shock to society, and the ensuing information problems had a huge impact on society at the same time. The urgent need to understand the Infodemic, i.e., the importance of the spread of false information related to the epidemic, has been highlighted. However, while there is a growing interest in this phenomenon, studies on the topic discovery, data collection, and data preparation phases of the information analysis process have been lacking.

OBJECTIVE

Since the epidemic is unprecedented and has not ended to this day, we aimed to examine the existing Infodemic-related literature from January 2019 to December 2022.

METHODS

We have systematically searched ScienceDirect and IEEE Xplore databases with some search limitations. From the searched literature we selected titles, abstracts and keywords, and limitations sections. We conducted an extensive structured literature search and analysis by filtering the literature and sorting out the available information.

RESULTS

A total of 47 papers ended up meeting the requirements of this review. Researchers in all of these literatures encountered different challenges, most of which were focused on the data collection step, with few challenges encountered in the data preparation phase and almost none in the topic discovery section. The challenges were mainly divided into the points of how to collect data quickly, how to get the required data samples, how to filter the data, what to do if the data set is too small, how to pick the right classifier and how to deal with topic drift and diversity. In addition, researchers have proposed partial solutions to the challenges, and we have also proposed possible solutions.

CONCLUSIONS

This review found that Infodemic is a rapidly growing research area that attracts the interest of researchers from different disciplines. The number of studies in this field has increased significantly in recent years, with researchers from different countries, including the United States, India, and China. Infodemic topic discovery, data collection, and data preparation are not easy, and each step faces different challenges. While there is some research in this emerging field, there are still many challenges that need to be addressed. These findings highlight the need for more articles to address these issues and fill these gaps.

Construction of influencing factors model for public information avoidance behavior in major infectious disease outbreaks based on meta-ethnography

Objective

Major infectious disease outbreaks are highly susceptible to diffuse outbreaks due to their sudden and more widespread nature. Compared to previous outbreaks such as the Spanish flu and SARS in China, COVID-19 has greatly affected the health of citizens and the economic development of countries worldwide, and is representative of major infectious disease outbreaks in many ways. Information avoidance, a common information behaviour during major infectious disease outbreaks, can alleviate the stress caused by information overload as a strategy to reduce negative emotions and maintain optimism. However, it can also bias risk perceptions and avoid content of greater value. Therefore, a deeper understanding of public information behaviour, particularly how and why relevant information is circumvented, places a demand on researchers.

Methods

A meta-ethnographic qualitative research methodology was used, and the seven steps of the methodology were strictly followed, including identifying integration themes, defining the connotations of integration themes, reading original studies, identifying relationships between studies, inter-translation between studies, synthetic translation, and presenting integration results. 26 original studies were integrated in a unified research framework, with a macro perspective that integrates consistent as well as complex and even contradictory findings and identifies dominant factors.

Conclusions

Identify demographic factors, information literacy, risk perception, cognitive structure, information quality, information sources, external characteristics of information, and environmental characteristics sub-dimensions around the dimensions of 'individual', 'information' and 'environment'. The study also explored the factors under each sub-dimension. The study finally identified three dimensions, nine sub-dimensions and 26 factors, and obtained a more complete theoretical framework to construct a "model of factors influencing public information avoidance behaviour in major infectious disease epidemics", with a view to providing a theoretical basis and practical reference for relevant departments in guiding public information behaviour and health practices in major infectious disease epidemics.