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Nurses' knowledge and protective health behaviors about prevention of COVID-19 pandemic complications at Baghdad Teaching Hospitals
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BACKGROUND:

Coronavirus disease (COVID-19) is a global pandemic caused by the severe acute respiratory syndrome coronavirus, SARS-CoV-2. Infection with SARS-CoV-2 primarily occurs through binding to angiotensin-converting enzyme-2 (ACE2), which is abundantly expressed in various anatomical sites, including the nasopharynx, lungs, cardiovascular system, and gastrointestinal and genitourinary tracts. This study aimed to nurses' knowledge and protective health behaviors about prevention of covid-19 pandemic complications.

MATERIALS AND METHODS:

A descriptive design study was conducted from October 1, 2022, to March 15, 2023. A non-probability purposive sample of 50 nurses from different educational levels, genders, and departments at Baghdad teaching hospitals was selected. Data collected were analyzed using SPSS version 25.0.

RESULTS:

The study comprised an equal distribution of male and female participants, with the majority (44%) aged between 22 and 30 years. A significant proportion (52%) of nurses held institute degrees. Forty-six percent were single, and the majority (74%) had 1-5 years of experience. The mean scores were 0.70 for nurses’ knowledge and 3.22 for nurses’ protective health behaviors.

CONCLUSIONS:

The findings indicate that nurses possess satisfactory knowledge and exhibit high levels of protective health behaviors regarding complications associated with the COVID-19 pandemic. It is recommended to conduct future research focusing on nurses’ knowledge and practices related to patient care in the context of a pandemic.

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Patients infected with the COVID-19 virus develop severe pneumonia, which typically results in death. Radiological data show that the disease involves interstitial lung involvement, lung opacities, bilateral ground-glass opacities, and patchy opacities. This study aimed to improve COVID-19 diagnosis via radiological chest X-ray (CXR) image analysis, making a substantial contribution to the development of a mobile application that efficiently identifies COVID-19, saving medical professionals time and resources. It also allows for timely preventative interventions by using more than 18000 CXR lung images and the MobileNetV2 convolutional neural network (CNN) architecture. The MobileNetV2 deep-learning model performances were evaluated

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