The physical and mental health of the medical staff in Wuhan Huoshenshan Hospital during COVID-19 epidemic: A Structural Equation Modeling approach

Wang, Jinyao; Li, Danhong; Bai, Xiumei; Cui, Jun; Ling, Yang; Mu, Xin; Yang, Rong

doi:10.1016/j.eujim.2021.101323

Cited by 20 publications

(23 citation statements)

References 49 publications

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“…In addition, it is worth mentioning that our results are in line with those of other authors who used structural equations models during the pandemic in other populations. Thus, they are consistent with the results of Wang et al, 2021 , which found a direct negative relationship of resilience to stress and anxiety in a sample of medical personnel in a hospital in Wuhan, Hubei Province's provincial capital (China)—the location of the first outbreak of COVID-19. They also support the results of Rodríguez-Hidalgo et al ( 2020 ), who showed a direct relationship between stress and anxious symptoms in university students throughout the COVID-19, again through a structural equation model.…”

Section: Discussionsupporting

confidence: 91%

“…Resilience indeed represents an individual’s set of personal resources allowing them to optimally face stressors and difficulties (Fletcher & Sarkar, 2013 ; Newman, 2005 ). Thus, several investigations have discovered a negative association between resilience and anxiety during pregnancy (Lubián López et al, 2021 ), and between resilience towards stress and anxiety in other populations at the time of the pandemic (Braun-Lewensohn et al, 2021 ; Satici et al, 2020 : Wang et al, 2021 ). Additionally, previous researches carried out before the pandemic have proven the protective role of resilience regarding these variables in the perinatal stage (Armans et al, 2020 ; García-León et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

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Resilience, stress and anxiety in pregnancy before and throughout the pandemic: a structural equation modelling approach

Puertas-Gonzalez

Mariño-Narvaez²,

Romero-González

et al. 2022

Curr Psychol

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The present study explored and compared the link between resilience and pregnancy-related stress, perceived stress, and anxiety, employing two structural equation models. One model focused on pregnant women before the outbreak of the pandemic, and the other on pregnancies throughout the pandemic. For this purpose, a total sample of 690 women during their pregnancy were collected: the Pre-Pandemic Group (P-PG) was composed of 341 pregnant women evaluated prior to the pandemic; and 349 pregnant women assessed at the time of the pandemic constituted the Pandemic Group (PG). The resilience, pregnancy-related stress, perceived stress, and anxiety symptomatology of the women were assessed. For both samples, resilience was found to lower levels of pregnancy-specific stress, as well as general perceived stress, and anxiety symptomatology. Furthermore, pregnancy-specific stress and perceived stress showed a covariance relationship and, that these, in turn, increased the anxiety. Moreover, the PG showed greater levels of pregnancy-specific stress, anxiety, somatisations, and obsessions-compulsions, while the P-PG presented higher perceived stress levels.

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Section: Discussionsupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Resilience, stress and anxiety in pregnancy before and throughout the pandemic: a structural equation modelling approach

Puertas-Gonzalez

Mariño-Narvaez²,

Romero-González

et al. 2022

Curr Psychol

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“…The SRSS is a self-reported sleep quality measurement tailored for Chinese people (28). Its reliability and validity have been proven by several previous studies (29,30). The SRSS includes ten items with a score ranging from the best sleep quality (one point) to the worst sleep quality (five points).…”

Section: Assessment Of Sleep Qualitymentioning

confidence: 71%

What Factors Are Most Closely Associated With Mood Disorders in Adolescents During the COVID-19 Pandemic? A Cross-Sectional Study Based on 1,771 Adolescents in Shandong Province, China

et al. 2021

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Background and Aims: COVID-19 has been proven to harm adolescents' mental health, and several psychological influence factors have been proposed. However, the importance of these factors in the development of mood disorders in adolescents during the pandemic still eludes researchers, and practical strategies for mental health education are limited.Methods: We constructed a sample of 1,771 adolescents from three junior high middle schools, three senior high middle schools, and three independent universities in Shandong province, China. The sample stratification was set as 5:4:3 for adolescent aged from 12 – 15, 15 – 18, 18 – 19. We examined the subjects' anxiety, depression, psychological resilience, perceived social support, coping strategies, subjective social/school status, screen time, and sleep quality with suitable psychological scales. We chose four widely used classification models-k-nearest neighbors, logistic regression, gradient-boosted decision tree (GBDT), and a combination of the GBDT and LR (GBDT + LR)-to construct machine learning models, and we utilized the Shapley additive explanations value (SHAP) to measure how the features affected the dependent variables. The area under the curve (AUC) of the receiver operating characteristic (ROC) curves was used to evaluate the performance of the models.Results: The current rates of occurrence of symptoms of anxiety and depression were 28.3 and 30.8% among the participants. The descriptive and univariate analyses showed that all of the factors included were statistically related to mood disorders. Among the four machine learning algorithms, the GBDT+LR algorithm achieved the best performance for anxiety and depression with average AUC values of 0.819 and 0.857. We found that the poor sleep quality was the most significant risk factor for mood disorders among Chinese adolescents. In addition, according to the feature importance (SHAP) of the psychological factors, we proposed a five-step mental health education strategy to be used during the COVID-19 pandemic (sleep quality-resilience-coping strategy-social support-perceived social status).Conclusion: In this study, we performed a cross-sectional investigation to examine the psychological impact of COVID-19 on adolescents. We applied machine learning algorithms to quantify the importance of each factor. In addition, we proposed a five-step mental health education strategy for school psychologists.

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“…The long-term exposure to medication procedures such as intubation and nebulization also leads to the high percentage of fatigue by fear of the hospital-acquired infections ( 16 , 17 ). Therefore, reducing the fatigue by tailored measures is critical to promote nurses' safety and health ( 18 ), and the environmental interventions could become a promising approach.…”

Section: Introductionmentioning

confidence: 99%

The Environmental Factors Associated With Fatigue of Frontline Nurses in the Infection Disease Nursing Unit

Adeney

Long

et al. 2021

Front. Public Health

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The workload in the Infection Disease Nursing Unit (IDNU) is increasing dramatically due to COVID-19, and leads to the prevalence of fatigue among the frontline nurses, threatening their health, and safety. The built environment and design could fundamentally affect the fatigue of nurses for a long-term perspective. This article aims to extract the environmental factors of IDNU and explore nurses' perceptions of these factors on the work-related fatigue. It would produce evidences for mitigating the fatigue by environmental interferons. A cross-sectional design was employed by combination of focus group interview and written survey. Environmental factors of IDNU were collected from healthcare design experts (n = 8). Nurses (n = 64) with frontline COVID-19 experiences in IDNU were recruited to assess these factors individually. Four environmental factors were identified as: Nursing Distance (ND), Spatial Crowdness (SC), Natural Ventilation, and Light (NVL), and Spatial Privacy (SP). Among them, ND was considered as the most influential factor on the physical fatigue, while SP was on the psychological fatigue. Generally, these environmental factors were found to be more influential on the physical fatigue than the psychological fatigue. Technical titles were found to be associated with the nurses' perceptions of fatigue by these environmental factors. Nurse assistant and practical nurse were more likely to suffer from the physical fatigue by these factors than senior nurse. The result indicated that environmental factors of IDNU were associated with the nurses' fatigue, particularly on the physical aspect. Environmental interventions of design could be adopted to alleviate the fatigue by these factors such as reducing the ND and improving the spatial privacy. The accurate interventional measures should be applied to fit nurses' conditions due to their technical titles. More attention should be given to the low-ranking nurses, who account for the majority and are much vulnerable to the physical fatigue by environmental factors.

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The physical and mental health of the medical staff in Wuhan Huoshenshan Hospital during COVID-19 epidemic: A Structural Equation Modeling approach

Cited by 20 publications

References 49 publications

Resilience, stress and anxiety in pregnancy before and throughout the pandemic: a structural equation modelling approach

Resilience, stress and anxiety in pregnancy before and throughout the pandemic: a structural equation modelling approach

What Factors Are Most Closely Associated With Mood Disorders in Adolescents During the COVID-19 Pandemic? A Cross-Sectional Study Based on 1,771 Adolescents in Shandong Province, China

The Environmental Factors Associated With Fatigue of Frontline Nurses in the Infection Disease Nursing Unit

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