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10.1016/j.jiph.2021.08.024

http://scihub22266oqcxt.onion/10.1016/j.jiph.2021.08.024
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34462221!8386094!34462221
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suck abstract from ncbi


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pmid34462221      J+Infect+Public+Health 2021 ; 14 (10): 1398-1403
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  • The Health Belief Model as an explanatory framework for COVID-19 prevention practices #MMPMID34462221
  • Alagili DE; Bamashmous M
  • J Infect Public Health 2021[Oct]; 14 (10): 1398-1403 PMID34462221show ga
  • BACKGROUND: The COVID-19 government public health measures are necessary to prevent the spread of COVID-19, however, their efficacy is largely dependent on adherence. This study utilized the Health Belief Model (HBM) to explain the public's adopted prevention practices during the COVID-19 outbreak in Saudi Arabia. METHODS: This study used "COVID-19 Snapshot MOnitoring (COSMO): monitoring knowledge, risk perceptions, preventive behaviours, and public trust in the current coronavirus outbreak" research protocol which is developed by the WHO Regional Office for Europe and the COSMO group. The COSMO questionnaire was translated into Arabic and distributed as an online survey via WhatsApp instant messaging application from May 2nd to May 26th, 2020. The dependent variable was the mean of adopting ten COVID-19 preventive practices. These practices were hand washing, avoiding touching eyes, nose and mouth, use of hand sanitizer, covering mouth and nose when coughing/sneezing, staying home when sick, avoiding close contact with infected persons, social distancing, wearing masks, home isolation, and lockdown. The independent variables included the HBM constructs (susceptibility to and severity of COVID-19, benefits of and barriers to adopting preventive behaviors, cues to action, health motivation, and self-efficacy), sociodemographic factors, presence of chronic illness, and perceived and actual knowledge. We conducted bivariate and multivariate analyses and reported significant findings (P
  • |*COVID-19[MESH]
  • |Communicable Disease Control[MESH]
  • |Cross-Sectional Studies[MESH]
  • |Health Belief Model[MESH]
  • |Humans[MESH]


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