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10.1111/inm.12817 http://scihub22266oqcxt.onion/10.1111/inm.12817 33491299!8014285!33491299     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Int+J+Ment+Health+Nurs 2021 ; 30 (2): 524-532 Nephropedia Template TP {{tp|p=33491299|t=2021. Predictive factors of anxiety and depression among nurses fighting coronavirus disease 2019 in China |pdf=|usr=}}{{33491299}} gab.com Text Predictive factors of anxiety and depression among nurses fighting coronavirus disease 2019 in China JO: Int J Ment Health Nurs http://www.kidney.de/pget.php?&tw=1&pmid=33491299 #FOAvip Anxiety and depression are common mental illnesses among nurses fighting coronavirus disease 2019 (COVID-19). However, the precise factors that affect anxiety and depression in this population require further evaluation. This study aimed to explore factors associated with anxiety and depression among nurses fighting COVID-19 in China. We used convenience sampling to recruit 282 nurses fighting COVID-19 in three hospitals. Participants were questioned about demographic characteristics, daily working time, daily sleep duration, sleep quality, anxiety, depression, resilience, and coping styles. Linear regression analysis indicated that resilience (beta = -0.217, P < 0.001), positive coping style (beta = -0.281, P < 0.001), negative coping style (beta = 0.395, P < 0.001), and sleep quality (beta = 0.153, P = 0.010) were predictive factors for anxiety, and the model explained 44.20% (P < 0.001) of variability. Resilience (beta = -0.239, P < 0.001), positive coping style (beta = -0.222, P < 0.001), negative coping style (beta = 0.152, P < 0.001), and sleep quality (beta = 0.104, P = 0.003) were identified as explanatory factors for depression, and the model explained 34.50% (P < 0.001) of variability. The present study suggested that resilience, coping styles, and sleep quality could account for an individual's levels of anxiety and depression. Twit Text FOAVip Predictive factors of anxiety and depression among nurses fighting coronavirus disease 2019 in China ????Int J Ment Health Nurs http://www.kidney.de/pget.php?&tw=1&pmid=33491299 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33491299 #FOAvip English Wikipedia <ref>{{Cite pmid|33491299}}</ref> Predictive factors of anxiety and depression among nurses fighting coronavirus disease 2019 in China #MMPMID33491299 Pang Y; Fang H; Li L; Chen M; Chen Y; Chen M Int J Ment Health Nurs 2021[Apr]; 30 (2): 524-532 PMID33491299show ga Anxiety and depression are common mental illnesses among nurses fighting coronavirus disease 2019 (COVID-19). However, the precise factors that affect anxiety and depression in this population require further evaluation. This study aimed to explore factors associated with anxiety and depression among nurses fighting COVID-19 in China. We used convenience sampling to recruit 282 nurses fighting COVID-19 in three hospitals. Participants were questioned about demographic characteristics, daily working time, daily sleep duration, sleep quality, anxiety, depression, resilience, and coping styles. Linear regression analysis indicated that resilience (beta = -0.217, P < 0.001), positive coping style (beta = -0.281, P < 0.001), negative coping style (beta = 0.395, P < 0.001), and sleep quality (beta = 0.153, P = 0.010) were predictive factors for anxiety, and the model explained 44.20% (P < 0.001) of variability. Resilience (beta = -0.239, P < 0.001), positive coping style (beta = -0.222, P < 0.001), negative coping style (beta = 0.152, P < 0.001), and sleep quality (beta = 0.104, P = 0.003) were identified as explanatory factors for depression, and the model explained 34.50% (P < 0.001) of variability. The present study suggested that resilience, coping styles, and sleep quality could account for an individual's levels of anxiety and depression. |Adaptation, Psychological[MESH] |Adult[MESH] |Anxiety/epidemiology/*etiology[MESH] |COVID-19/*nursing/psychology[MESH] |China/epidemiology[MESH] |Cross-Sectional Studies[MESH] |Depression/epidemiology/*etiology[MESH] |Humans[MESH] |Middle Aged[MESH] |Nursing Staff, Hospital/*psychology/statistics & numerical data[MESH] |Occupational Diseases/epidemiology/*etiology/psychology[MESH] |Psychiatric Status Rating Scales[MESH] |Psychological Tests[MESH] |Resilience, Psychological[MESH] |Risk Factors[MESH] |Surveys and Questionnaires[MESH]Predictive factors of anxiety and depression among nurses fighting cor(epmc).pdf DeepDyve Pubget Overpricing