Introduction: Following the global spread of the Covid-19 infection, the Iranian government adopted measures to control the spread of the disease, but they were not applicable without the acceptance and interaction of the general population. This study used the Extended Parallel Process Model (EPPM) components to attempt to determine risk communication and risk perception along with its influencing factors in Covid-19 disease among the population of northwestern Iran.
Method: This cross-sectional study was conducted among the general population of the province. Demographic characteristics and extended parallel process model questionnaires were used to collect data, which was then analyzed based on descriptive (frequency, mean, standard deviation) and inferential statistics (-test, analysis of variance, regression, chi-square) in SPSS-25 software.
Results: This study showed that 63.8% of the participants continually followed Covid-19 news, and 34% of participants used social media to get the news and warnings related to the Covid-19 pandemic. Among the domains of participants' risk perception for Covid-19 disease, the three domains of self-efficacy, response effectiveness and intention had the highest means compared with other domains. Significant correlations were found between risk perception and the dimensions of age, gender, marriage status, number of family members, place of residence, underlying disease, history of Covid-19, and family history of Covid-19 disease ( < 0.05). Multivariate linear regression analysis revealed that perceived sensitivity, perceived severity, self-efficacy, fear, defensive avoidance, intention, and behaviors were independent predictors of response efficacy ( < 0.001).
Conclusion: More than two years after the onset of the spread of Covid-19 disease, the risk perception of the disease among the study population was still insufficient in many areas. Risk of communication refers to the point of interaction between the government and the people, and the need to improve public trust in this issue is strongly felt.
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http://dx.doi.org/10.1016/j.ijdrr.2023.103547 | DOI Listing |
JMIR 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.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
The Jackson Laboratory, Bar Harbor, ME, USA.
Background: Alzheimer's disease (AD) and AD-related dementias (ADRD) are modulated by gene-environment (GxE) interactions across the lifespan. Variants of specific genes increase AD risk and synergize with lifetime exposure to environmental toxicants ("exposome"), including neurotoxic metals (lead, Pb; cadmium, Cd) and metalloid (As). These metal/metalloid toxicants readily enter the body (e.
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