Knowledge on COVID-19 among Nursing Students of a Selected Nursing Institute of Dhaka City, Bangladesh.

Autor: Akhter, Afroza, Banu, Bilkis, Akter, Nasrin, Chawdhury, Sujana Haque, Antara, Labanna Paul, Hossain, Sarder Mahmud
Předmět:
Zdroj: Indian Journal of Public Health Research & Development; Jan-Mar2022, Vol. 13 Issue 1, p58-68, 11p
Abstrakt: Background: Bangladesh is trying to shape out coronavirus disease of 2019 (COVID-19) pandemic with limited frontiers resources science March 2020. Among all frontier, Bangladeshi nurses are also playing a dynamic role to control infection through direct contact with COVID patients. Objective: This research aims to identify the level and predictors of poor knowledge of nursing students toward the COVID-19. Method: This study was a quantitative type of cross-sectional study with 150 participants randomly selected from 226 students of the Armed Forces Medical Institute located in Dhaka Cantonment of Dhaka city of Bangladesh. Data were collected by using a pre-tested questionnaire through a telephonic interview by trained and experienced interviewers. Analysis was done by using univariate, multivariate techniques followed by regression modeling. Result: Overall level of knowledge was observed poor (67.3%) among more than half of BSc nursing students. A greater part of nursing students got poor knowledge on the preventive measures to reduce transmission of COVID 19 (98.7%; 40.20±12.39) & management of COVID 19 (94.7%; 40.20±12.39). In terms of predicting the causes of poor knowledge, this study found that BSc nursing students of the second year (AOR= 2.53, p < 0.01) are more likely to have poor knowledge on COVID-19 compared to another educational group. Conclusion: Nurses are the frontiers to mitigate COVID-19 and manage the affected people effectively. Therefore, knowledge of them needs to be perfect to ensure the proper practice to prevent COVID-19. Thus, an enthusiastic and demonstrative learning system is required to make them knowledgeable enough against COVID-19. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index