Health care workers' perceptions of their hospitals’ preparedness during the COVID-19 virus pandemic in three different world regions

Alhadi, Bassem; Abdelrahman, Manal Mahmoud; Ali, Shammy Rashid; Taha, Khaled; Manzour, Ayat

doi:10.1016/j.cegh.2023.101278

Cited by 3 publications

(2 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The use of infection control equipment, establishment of outpatient clinics outside the hospital in order to manage symptomatic patients and isolation processes were some functional and process strategies ( 10 ). A hospital-based cross-sectional survey in Egypt, Saudi Arabia, the UAE, Qatar, Australia, the UK, and Ireland indicated the appropriate preparedness of hospitals during the COVID-19 from the perspectives of healthcare professionals ( 11 ). This qualitative study aimed to understand challenges during the COVID-19 pandemic and strategies used by healthcare professionals in Iranian hospitals.…”

Section: Introductionmentioning

confidence: 99%

Hospital response challenges and strategies during COVID-19 pandemic: a qualitative study

Mohammadinia,

Saadatmand,

Khaledi Sardashti

et al. 2023

Front. Public Health

View full text Add to dashboard Cite

BackgroundAt the beginning of the COVID-19 pandemic, healthcare managers at hospitals did not have sufficient experiences to appropriately respond to the COVID-19 outbreak. Due to a lack of preparedness, many challenges arose in the healthcare system, and each country developed and implemented strategies depending on national policies. This study aimed to understand challenges during the COVID-19 pandemic and strategies used in Iranian hospitals.MethodsA qualitative research was conducted in four hospitals in an urban area of Iran. In-depth semi-structured interviews were performed with 32 participants including healthcare managers, nurses, and medical doctors. Data underwent qualitative content analysis.ResultsFour categories were developed: ‘capacity expansion’, ‘management affairs’, ‘diagnostic services,” and therapeutic services’. Each category consisted of 2–3 subcategories.ConclusionHospitals should be prepared to intelligently respond to future epidemics. It is necessary to develop a comprehensive epidemic plan for the management of disasters to reduce the impact of the epidemic and minimize the risk to public health and ensure that resources are allocated in an efficient and effective manner.

show abstract

Section: Introductionmentioning

confidence: 99%

Hospital response challenges and strategies during COVID-19 pandemic: a qualitative study

Mohammadinia,

Saadatmand,

Khaledi Sardashti

et al. 2023

Front. Public Health

View full text Add to dashboard Cite

show abstract

“…In late 2019, in Hubei province, China, there were reports of many patients in hospitals suffering from pneumonia and respiratory failure due to a newly identified coronavirus named SARS-CoV-2. The World Health Organization later called this disease COVID-19 and declared it a pandemic in March 2020 (Alhadi et al, 2023). Although there have been different variants of COVID-19, such as Alpha, Beta, Delta, Gamma, Epsilon, Zeta, Eta, and omicron, some common symptoms presented by patients include headache, nasal congestion, fever, and fatigue, among others.…”

mentioning

confidence: 99%

Detection and risk assessment of COVID-19 through machine learning

Luna-Benoso,

Martínez-Perales,

Cortés-Galicia

et al. 2024

Int. j. adv. appl. sci.

View full text Add to dashboard Cite

COVID-19, also known as coronavirus disease, is caused by the SARS-CoV-2 virus. People infected with COVID-19 may show a range of symptoms from mild to severe, including fever, cough, difficulty breathing, tiredness, and nasal congestion, among others. The goal of this study is to use machine learning to identify if a person has COVID-19 based on their symptoms and to predict how severe their illness might become. This could lead to outcomes like needing a ventilator or being admitted to an Intensive Care Unit. The methods used in this research include Artificial Neural Networks (specifically, Multi-Layer Perceptrons), Classification and Regression Trees, and Random Forests. Data from the National Epidemiological Surveillance System of Mexico City was analyzed. The findings indicate that the Multi-Layer Perceptron model was the most accurate, with an 87.68% success rate. It was best at correctly identifying COVID-19 cases. Random Forests were more effective at predicting severe cases and those requiring Intensive Care Unit admission, while Classification and Regression Trees were more accurate in identifying patients who needed to be put on a ventilator.

show abstract

Evaluation of a massive open online course for just-in-time training of healthcare workers

Strehlow,

Johnston,

Aluri

et al. 2024

Front. Public Health

View full text Add to dashboard Cite

IntroductionCOVID-19 created a global need for healthcare worker (HCW) training. Initially, mass trainings focused on public health workers and physicians working in intensive care units. However, in resource-constrained settings, nurses and general practitioners provide most patient care, typically lacking the training and equipment to manage critically ill patients. We developed a massive open online course (MOOC) for HCWs in resource-constrained settings aimed at training bedside providers caring for COVID-19 patients. We describe the development, implementation and analysis of this MOOC.MethodsFrom May through June 2020, the course was developed by a multi-disciplinary team and launched on two online platforms in July. The 4-hour course comprises 6 video-based modules. Student knowledge was assessed using pre- and post-module quizzes and final exam, while demographics and user experience were evaluated by pre- and post-course surveys and learning platform data.ResultsFrom July 17th to September 24th, 30,859 students enrolled, 18,818 started, and 7,101 completed the course. Most participants worked in healthcare (78%) and resided in lower middle- (38%) or upper middle- (20%) income countries. Learners from upper middle-income and lower middle-income countries had higher completion rates. Knowledge gains were observed from pre-module to post-module quizzes and a final exam. Afterward, participants reported increased self-efficacy regarding course objectives, a 0.63 mean increase on a 4-point scale (95% CI [0.60,0.66]). Most participants (93%) would recommend the course to others.ConclusionThis article demonstrates the potential of MOOCs to rapidly provide access to emerging medical knowledge during a public health crisis, particularly for HCWs in high- and middle-income countries.

show abstract

Health care workers' perceptions of their hospitals’ preparedness during the COVID-19 virus pandemic in three different world regions

Cited by 3 publications

References 20 publications

Hospital response challenges and strategies during COVID-19 pandemic: a qualitative study

Hospital response challenges and strategies during COVID-19 pandemic: a qualitative study

Detection and risk assessment of COVID-19 through machine learning

Evaluation of a massive open online course for just-in-time training of healthcare workers

Contact Info

Product

Resources

About