Knowledge, Attitude, and Practices Toward COVID-19 in Primary Healthcare Providers: A Cross-Sectional Study from Three Tertiary Care Hospitals of Peshawar, Pakistan.
Iltaf HussainAbdul MajeedImran ImranMuhib UllahFurqan K HashmiHamid SaeedMuhammad O ChaudhryMuhammad Fawad RasoolPublished in: Journal of community health (2021)
An online cross-sectional study was carried out to evaluate the knowledge, attitude, and practice about coronavirus disease 2019 (COVID-19) among primary health care providers (PHPs) at three tertiary care hospital, Peshawar, Pakistan. Data was collected via email and online social media platforms. Statistical package for social science (SPSS) version 25.0 was used for data analysis. Among the total participants (n = 114), 74 (66.7%) were male and 37 (33.3%) were female. The mean scores for knowledge, attitude and practice were 12.7 ± 0.89, 8.9 ± 4.1 and 7.3 ± 1.2, respectively. Most of the participants knew the term COVID-19 and its mode of transmission (90%), signs and symptoms (84%) and risk factors (72%) associated with it. Most of the participants agreed that COVID-19 can be transmitted through coughing and sneezing (74.3%) and 84.6% were in favor that COVID-19 can be prevented by adopting preventive measures. Around 68.8% of the participants disagreed with the use of antibiotics in the prevention of COVID-19. Ninety percent of the respondents were avoiding close contact with the people having cough and flu-like symptoms. Most PHPs had good knowledge, positive attitude and reasonable practices regarding COVID-19. Moreover, focused training programs for PHPs at the Government level can further improve their understanding of risks and preventive strategies related to COVID-19, which will help them to provide appropriate care to their patients as well as to protect themselves from this infection.
Keyphrases
- coronavirus disease
- healthcare
- sars cov
- tertiary care
- social media
- respiratory syndrome coronavirus
- primary care
- data analysis
- public health
- health information
- palliative care
- ejection fraction
- machine learning
- mental health
- physical activity
- risk assessment
- depressive symptoms
- deep learning
- sleep quality
- quality improvement
- electronic health record
- gestational age