Selected article for: "access information and logistic regression"

Author: Asemahagn, Mulusew Andualem
Title: Factors determining the knowledge and prevention practice of healthcare workers towards COVID-19 in Amhara region, Ethiopia: a cross-sectional survey
  • Cord-id: dj8lw7at
  • Document date: 2020_8_20
  • ID: dj8lw7at
    Snippet: BACKGROUND: Healthcare workers (HWs) are at the highest risk of getting CIVID-19. This study aimed to assess factors determining the knowledge and prevention of HWs towards COVID-19 in the Amhara Region, Ethiopia. METHODS: A cross-sectional online survey was conducted among 442 HWs using email and telegram addresses. The knowledge and practice of HWs were estimated using 16 knowledge and 11 practice questions. A multivariable logistic regression analysis was used on SPSS version 25 to identify f
    Document: BACKGROUND: Healthcare workers (HWs) are at the highest risk of getting CIVID-19. This study aimed to assess factors determining the knowledge and prevention of HWs towards COVID-19 in the Amhara Region, Ethiopia. METHODS: A cross-sectional online survey was conducted among 442 HWs using email and telegram addresses. The knowledge and practice of HWs were estimated using 16 knowledge and 11 practice questions. A multivariable logistic regression analysis was used on SPSS version 25 to identify factors related to the knowledge and prevention practice of HWs on COVID-19. Significance was determined at a p value of < 0.05 and association was described by using odds ratio at 95% CI. RESULTS: Of 442 HWs, 398 (90% response rate) responded to the online interview questionnaire. From 398 HWs, 231(58%), 225(56%), 207(53%), and 191(48%) were males, from rural area, aged ≥ 34 years and nurses, respectively. About 279(70%) HWs had good knowledge of COVID-19 followed by 247(62%) good prevention practices. Age < 34 years (AOR = 2.14, 95% CI = 1.25–3.62), rural residence (AOR = 0.44, 95% CI = 0.26–0.70), access to infection prevention (IP) training (AOR = 2.4, 95% CI = 1.36–4.21), presence of IP guideline (AOR = 2.82, 95% CI = 1.64–4.62), and using social media (AOR = 2.51, 95% CI = 1.42–4.53) were factors of knowledge about COVID-19. Whereas, rural residence (AOR = 0.45, 95% CI = 0.31–0.75), facility type (AOR = 0.40, 95% CI = 0.28–0.89), access to IP training (AOR = 2.32, 95% CI = 1.35–4.16), presence of IP guidelines (AOR = 2.10, 95% CI = 1.21–3.45), knowledge about COVID-19 (AOR = 2.98, 95% CI = 2.15–5.27), having chronic illnesses (AOR = 2.0, 95% CI = 1.15–3.75), lack of protective equipment (PPE) (AOR = 0.42, 95% CI = 0.32–0.74), and high workload (AOR = 0.40, 95% CI = 0.36–0.87) were factors of COVID-19 prevention. CONCLUSION: In this study, most of the HWs had good knowledge but had lower prevention practice of COVID-19. Socio-demographic and access to information sources were factors of knowledge on COVID-19. Similarly, residence, shortage of PPE, high workload, comorbidities, knowledge, and access to IP training and guideline were factors limiting prevention practices. Thus, a consistent supply of PPE and improving health workers’ knowledge, making IP guidelines and information sources available, and managing chronic illnesses are crucial to prevent COVID-19 among HWs.

    Search related documents:
    Co phrase search for related documents
    • access training and adequate ppe: 1
    • access training and logistic regression: 1
    • access training and low knowledge: 1, 2, 3
    • addis ababa and adequate knowledge: 1
    • addis ababa and adequate ppe: 1, 2
    • addis ababa and logistic regression: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21
    • addis ababa and logistic regression analysis: 1, 2, 3, 4
    • addis ababa and logistic regression model: 1, 2, 3, 4, 5
    • addis ababa and low knowledge: 1
    • additional file and logistic regression: 1
    • additional file and logistic regression analysis: 1
    • adequate knowledge and logistic regression: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25
    • adequate knowledge and logistic regression analysis: 1, 2, 3, 4, 5, 6, 7, 8
    • adequate knowledge and logistic regression model: 1, 2, 3
    • adequate knowledge and low knowledge: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12
    • adequate ppe and logistic regression: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14
    • adequate ppe and logistic regression analysis: 1, 2, 3
    • adequate ppe and logistic regression model: 1, 2, 3
    • logistic regression and low knowledge: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25