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1.
Pakistan Journal of Medical and Health Sciences ; 16(7):438-440, 2022.
Article in English | EMBASE | ID: covidwho-2067742

ABSTRACT

Introduction: Pandemics affect people in a defeatist manner and become stressful for people with relatives which need specific forms of care and attention. The study was conducted to find out if anxiety prevails among caretakers during the Covid-19 Pandemic as according to the literature review caregivers experience burden and fears related to their care-recipients and telerehabilitation. Material and Methods: The study used cross sectional survey and quantitative research.50 care-givers participated in the research where they filled online questionnaires inspired and derived from care-giver burden scale and beck anxiety inventory. Anxiety was clearly evident as most of the care-givers agreed to have feelings of nervousness 19 (38%), feeling anxious 18 (36%), feeling distressed 22 (44%), complaints about emotional burden 23 (43%) and 23 (46%) constant immersion in duties towards care-recipients. Results: SPSS tables depict the analyzed results and their interpretation. The results show 36%of the care-givers agreed that they face anxiety when a situation gets out of control, 44% were distressed about not getting enough help from healthcare team and other family and friends, 55% are apprehensive about their present condition and 46% are emotionally challenged and constantly immersed in duties owing to their family members. Conclusions: Anxiety and depression as a result of caregiving burden is common among care-givers and needs to be addressed as soon as possible. This makes it essential that health professionals pay heed and attention to develop interventions for care-givers and provide them with pertinent knowledge.

2.
The Egyptian Journal of Bronchology ; 16(1), 2022.
Article in English | EuropePMC | ID: covidwho-1989265

ABSTRACT

Background Little information is available about the linkage between sleep affection and COVID-19. Preliminary reports and clinical observations focused on the appearance of related mental health issues, especially in healthcare workers (HCWs). Methods A cross-sectional study is conducted on the COVID-19 second-line HCWs using an English online survey prepared via Google forms. The survey focused on sociodemographic and profession-related characteristics (age, sex, smoking, history of previous sleep disorders or medications affecting sleep, comorbidities specialty, years of experience, and number of hours worked per week) and COVID-19-associated risks (being on the second line of COVID-19 management, following updates and news about COVID-19, and getting an infection with COVID-19 or having a colleague/friend who was infected with or died of COVID-19). Assessment of anxiety, insomnia, and sleep quality was done using the relevant diagnostic scales. Results This study included 162 second-line HCWs with a mean age of 34.36 ± 8.49 years. Although being in second lines, there was a high prevalence of anxiety (49.38%), insomnia (56.17%), and poor sleep quality (67.9%) during the pandemic. One condition was recently developed after the pandemic: insomnia in 6.6%, anxiety in 5.7%, and poor sleep in 16%. Two conditions were developed: insomnia and poor sleep in 21.7%, anxiety and poor sleep in 7.5%, and insomnia and anxiety in 10.4%. The three conditions were de novo experienced in 19.8%. A total of 22.4% of those who followed daily COVID-19 updates developed de novo combined anxiety, insomnia, and poor sleep. A total of 38.5% of participants that had been infected with COVID-19 developed de novo combined anxiety, insomnia, and poor sleep. A total of 50% of participants who had a colleague/friend who died with COVID-19 developed de novo combined anxiety, insomnia, and poor sleep. Conclusion Although being in second lines, there was a high prevalence of anxiety, depression, and poor sleep concerning COVID-19-related factors.

3.
Heliyon ; 7(10): e08143, 2021 Oct.
Article in English | MEDLINE | ID: covidwho-1520998

ABSTRACT

COVID-19 has produced a global pandemic affecting all over of the world. Prediction of the rate of COVID-19 spread and modeling of its course have critical impact on both health system and policy makers. Indeed, policy making depends on judgments formed by the prediction models to propose new strategies and to measure the efficiency of the imposed policies. Based on the nonlinear and complex nature of this disorder and difficulties in estimation of virus transmission features using traditional epidemic models, artificial intelligence methods have been applied for prediction of its spread. Based on the importance of machine and deep learning approaches in the estimation of COVID-19 spreading trend, in the present study, we review studies which used these strategies to predict the number of new cases of COVID-19. Adaptive neuro-fuzzy inference system, long short-term memory, recurrent neural network and multilayer perceptron are among the mostly used strategies in this regard. We compared the performance of several machine learning methods in prediction of COVID-19 spread. Root means squared error (RMSE), mean absolute error (MAE), R2 coefficient of determination (R2), and mean absolute percentage error (MAPE) parameters were selected as performance measures for comparison of the accuracy of models. R2 values have ranged from 0.64 to 1 for artificial neural network (ANN) and Bidirectional long short-term memory (LSTM), respectively. Adaptive neuro-fuzzy inference system (ANFIS), Autoregressive Integrated Moving Average (ARIMA) and Multilayer perceptron (MLP) have also have R2 values near 1. ARIMA and LSTM had the highest MAPE values. Collectively, these models are capable of identification of learning parameters that affect dissimilarities in COVID-19 spread across various regions or populations, combining numerous intervention methods and implementing what-if scenarios by integrating data from diseases having analogous trends with COVID-19. Therefore, application of these methods would help in precise policy making to design the most appropriate interventions and avoid non-efficient restrictions.

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