Volume 16, Issue 4 (Oct 2022)
payavard 2022, 16(4): 286-295 |
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Ethics code: IR.ZUMS.REC.1399.367
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Hasanzadeh F, Aghajanloo A, Dinmohammadi M. Factors Affecting the Acceptance of Personal Protection Measures among Patients before Having Covid-19. payavard 2022; 16 (4) :286-295
URL: http://payavard.tums.ac.ir/article-1-7313-en.html
URL: http://payavard.tums.ac.ir/article-1-7313-en.html
1- Master of Science in Critical Care Nursing, School of Nursing and Midwifery, Zanjan University of Medical Sciences, Zanjan, Iran
2- Assistant Professor, Department of Medical-Surgical Nursing, School of Nursing and Midwifery, Zanjan University of Medical Sciences, Zanjan, Iran
3- Associate Professor, Social Determinants of Health Research Center, Zanjan University of Medical Sciences, Zanjan, Iran ,mdinmohammadi@zums.ac.ir
2- Assistant Professor, Department of Medical-Surgical Nursing, School of Nursing and Midwifery, Zanjan University of Medical Sciences, Zanjan, Iran
3- Associate Professor, Social Determinants of Health Research Center, Zanjan University of Medical Sciences, Zanjan, Iran ,
Abstract: (1081 Views)
Background and Aim: The most recent threat to the global community is the ongoing outbreak of the disease known as Coronavirus Disease 2019 (COVID-19). Taking personal protection measures (PPM) is crucial to prevent the spread of COVID-19. This study aimed to determine the factors affecting the acceptance of PPM among patients before having COVID-19 admitted to hospitals in northwestern of Iran.
Materials and Methods: In this cross-sectional study, 560 eligible patients with a diagnosis of COVID-19 were selected by convenience sampling. Data were collected from January 6 to May 21, 2021, through a researcher-made questionnaire and were analyzed with descriptive statistics (number, percentage, mean, standard deviation), independent t-test, and multiple linear regression by SPSS. A significance level of 5% was selected.
Results: The average age of the participants was 60.1 (16.01) years with an age range of 21 to 95 years. Most of the participants were male (53.9%) and married (91.8%). The mean score of PPM acceptance among patients was 16.55 (2.59) out of 20 points. Multiple linear regression analysis determined employment status (β=0.29, P<0.001), residency (β=-0.19, P<0.001), education level (β=0.11, P=0.048), smoking (β=-0.10, P=0.03) and income level (β=0.13, P=0.01), as predictors of acceptance of PPM. Other personal and occupational variables, including age, gender, marriage, living status, having children, and history of influenza vaccination, were not found to be effective in predicting the acceptance of personal protection measures among participating patients.
Conclusion: The acceptance of personal protection measures among patients was relatively high. Acceptance of the unemployed, rural residents, illiterate, smokers, and those with low-income level was low. This study emphasizes the need to pay attention to the changes in the individual, social and economic characteristics of the community and their effects on preventive health behaviors, especially in vulnerable groups.
Materials and Methods: In this cross-sectional study, 560 eligible patients with a diagnosis of COVID-19 were selected by convenience sampling. Data were collected from January 6 to May 21, 2021, through a researcher-made questionnaire and were analyzed with descriptive statistics (number, percentage, mean, standard deviation), independent t-test, and multiple linear regression by SPSS. A significance level of 5% was selected.
Results: The average age of the participants was 60.1 (16.01) years with an age range of 21 to 95 years. Most of the participants were male (53.9%) and married (91.8%). The mean score of PPM acceptance among patients was 16.55 (2.59) out of 20 points. Multiple linear regression analysis determined employment status (β=0.29, P<0.001), residency (β=-0.19, P<0.001), education level (β=0.11, P=0.048), smoking (β=-0.10, P=0.03) and income level (β=0.13, P=0.01), as predictors of acceptance of PPM. Other personal and occupational variables, including age, gender, marriage, living status, having children, and history of influenza vaccination, were not found to be effective in predicting the acceptance of personal protection measures among participating patients.
Conclusion: The acceptance of personal protection measures among patients was relatively high. Acceptance of the unemployed, rural residents, illiterate, smokers, and those with low-income level was low. This study emphasizes the need to pay attention to the changes in the individual, social and economic characteristics of the community and their effects on preventive health behaviors, especially in vulnerable groups.
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