Objectives: To describe changes in public risk perception and risky behaviours during the first wave (W1) and second wave (W2) of COVID-19 in Nigeria, associated factors and observed trend of the outbreak.
Design: A secondary data analysis of cross-sectional telephone-based surveys conducted during the W1 and W2 of COVID-19 in Nigeria.
Setting: Nigeria.
Participants: Data from participants randomly selected from all states in Nigeria.
Primary Outcome: Risk perception for COVID-19 infection categorised as risk perceived and risk not perceived.
Secondary Outcome: Compliance to public health and social measures (PHSMs) categorised as compliant; non-compliant and indifferent.
Analysis: Comparison of frequencies during both waves using χ statistic to test for associations. Univariate and multivariate logistic regression analyses helped estimate the unadjusted and adjusted odds of risk perception of oneself contracting COVID-19. Level of statistical significance was set at p<0.05.
Results: Triangulated datasets had a total of 6401 respondents, majority (49.5%) aged 25-35 years. Overall, 55.4% and 56.1% perceived themselves to be at risk of COVID-19 infection during the W1 and W2, respectively. A higher proportion of males than females perceived themselves to be at risk during the W1 (60.3% vs 50.3%, p<0.001) and the W2 (58.3% vs 52.6%, p<0.05). Residing in the south-west was associated with not perceiving oneself at risk of COVID-19 infection (W1-AOdds Ratio (AOR) 0.28; 95% CI 0.20 to 0.40; W2-AOR 0.71; 95% CI 0.52 to 0.97). There was significant increase in non-compliance to PHSMs in the W2 compared with W1. Non-compliance rate was higher among individuals who perceived themselves not to be at risk of getting infected (p<0.001).
Conclusion: Risk communication and community engagement geared towards increasing risk perception of COVID-19 should be implemented, particularly among the identified population groups. This could increase adherence to PHSMs and potentially reduce the burden of COVID-19 in Nigeria.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8977458 | PMC |
http://dx.doi.org/10.1136/bmjopen-2021-058747 | 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.
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