In crisis communication, warning messages are key to prevent or mitigate damage by informing the public about impending risks and hazards. The present study explored the influence of hazard type, trait anxiety, and warning message on different components of risk perception. A survey examined 614 German participants (18-96 years, M = 31.64, 63.0% female) using a pre-post comparison. Participants were randomly allocated to one of five hazards (severe weather, act of violence, breakdown of emergency number, discovery of a World War II bomb, or major fire) for which they received a warning message. Four components of risk perception (perceived severity, anticipatory worry, anticipated emotions, and perceived likelihood) were measured before and after the receipt. Also, trait anxiety was assessed. Analyses of covariance of risk perception were calculated, examining the effect of warning message, trait anxiety, and hazard type while controlling for age, gender, and previous hazard experience. Results showed main effects of hazard type and trait anxiety on every component of risk perception, except for perceived likelihood. The receipt of a warning message led to a significant decrease in anticipated negative emotions. However, changes across components of risk perception, as well as hazards, were inconsistent, as perceived severity decreased while perceived likelihood and anticipatory worry increased. In addition, three interactional effects were found (perceived severity × hazard type, perceived severity × trait anxiety, and anticipated emotions × hazard type). The findings point toward differences in the processing of warning messages yet underline the importance of hazard type, as well as characteristics of the recipient.
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http://dx.doi.org/10.1111/risa.13636 | DOI Listing |
Psychol Rep
January 2025
Department of Clinical Psychology, Seattle Pacific University, Seattle, WA, USA.
This study investigated whether parental socialization of negative emotions moderated the relationship between adolescents' low executive function or high impulsivity and their current or subsequent emotion dysregulation. Emotion dysregulation, characterized by difficulties in managing the intensity and duration of emotions, is a transdiagnostic factor linked to adverse outcomes. Youth with poor executive functioning and/or high impulsivity are at risk for emotion dysregulation; however, the role of parenting in influencing this trajectory warrants exploration.
View Article and Find Full Text PDFJMIR Form Res
December 2024
Pharmacy Department, Gold Coast Hospital and Health Service, Southport, Australia.
Background: Artificial intelligence (AI) has the potential to address growing logistical and economic pressures on the health care system by reducing risk, increasing productivity, and improving patient safety; however, implementing digital health technologies can be disruptive. Workforce perception is a powerful indicator of technology use and acceptance, however, there is little research available on the perceptions of allied health professionals (AHPs) toward AI in health care.
Objective: This study aimed to explore AHP perceptions of AI and the opportunities and challenges for its use in health care delivery.
Math Biosci
January 2025
Biocomplexity Institute, University of Virginia, VA, USA; Department of Computer Science, University of Virginia, VA, USA.
Public health interventions reduce infection risk, while imposing significant costs on both individuals and the society. Interventions can also lead to behavioral changes, as individuals weigh the cost and benefits of avoiding infection. Aggregate epidemiological models typically focus on the population-level consequences of interventions, often not incorporating the mechanisms driving behavioral adaptations associated with interventions compliance.
View Article and Find Full Text PDFPLoS One
January 2025
School of Economics & Management, Beijing Information Science & Technology University, Beijing, China.
E-commerce faces challenges such as content homogenization and high perceived risk among users. This paper aims to predict perceived risk in different contexts by analyzing review content and website information. Based on a dataset containing 262,752 online reviews, we employ the KeyBERT-TextCNN model to extract thematic features from the review content.
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