scholarly journals Anomaly Variation of Vegetation and Its Influencing Factors in Mainland China During ENSO Period

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 721-734 ◽  
Author(s):  
Qingzhi Zhao ◽  
Xiongwei Ma ◽  
Wanqiang Yao ◽  
Yang Liu ◽  
Yibin Yao
Keyword(s):  
Influencing Factors ◽  
Mainland China

Hemodialysis nurse burnout from 31 provinces in mainland China: a cross-sectional survey (Preprint)

2020 ◽  
Author(s):  
Lifang Zhou ◽  
Weiai Guo ◽  
Li Song ◽  
Guanrong Zhang ◽  
Mi Zhong ◽  
...  
Keyword(s):  
Influencing Factors ◽  
Interpersonal Relationships ◽  
Mainland China ◽  
Maslach Burnout Inventory ◽  
Job Burnout ◽  
Equation Modeling ◽  
Nurse Training ◽  
Cross Sectional ◽  
Specialist Nurse ◽  
Nurse Burnout

BACKGROUND Job burnout is an occupational illness with high prevalence among nurses in China. The job burnout status among hemodialysis nurses should be given more attention because of they handle sophisticated machinery, and there is a high risk of infection in hemodialysis nursing. OBJECTIVE The level and influencing factors of job burnout among hemodialysis nurses in mainland China were investigated. METHODS This was a cross-sectional study conducted in all 31 provinces of mainland China in 2019. Data from nurses responsible for direct care in 2738 hemodialysis units were analyzed. An effective response rate of 99.00% (10570/10677) was achieved. Hemodialysis nurse burnout was measured by the Maslach Burnout Inventory. Working atmosphere and interpersonal relationships with colleagues were each measured by a single question. Multiple linear regression analysis was used to explore the factors related to nurse burnout. Structural equation modeling was used to explore the effect of the working environment, individual factors, and specialist nurse training on the HD nurse burnout and the intention to leave. RESULTS The total burnout score was 38.69 (SD17.47), indicating that the level of job burnout among hemodialysis nurses in mainland China was relatively low. Hemodialysis nurses experienced low-level burnout on the emotional exhaustion and depersonalization subscale and high-level burnout on the personal accomplishment subscale. Statistically significant differences in burnout levels were associated with working atmosphere, interpersonal relationships with colleagues, specialist nurse training, number of children, night shift, and marital status. CONCLUSIONS The burnout level of HD nurses in mainland China was relatively low. Working atmosphere, interpersonal relationships with colleagues, and training of specialist nurses are the most important influencing factors regarding job burnout in hemodialysis nurses. Therefore, it is suggested that improving the working atmosphere and interpersonal relationship processing ability and providing more training opportunities for nurses can alleviate job burnout in nurses.

scholarly journals Using an Eigenvector Spatial Filtering-Based Spatially Varying Coefficient Model to Analyze the Spatial Heterogeneity of COVID-19 and Its Influencing Factors in Mainland China

2022 ◽  
Vol 11 (1) ◽  
pp. 67
Author(s):  
Meijie Chen ◽  
Yumin Chen ◽  
John P. Wilson ◽  
Huangyuan Tan ◽  
Tianyou Chu
Keyword(s):  
Risk Factors ◽  
Health Risk ◽  
Spatial Heterogeneity ◽  
Influencing Factors ◽  
Mainland China ◽  
Spatial Filtering ◽  
Varying Coefficient Model ◽  
Health Risk Factors ◽  
Eigenvector Spatial Filtering ◽  
Spatially Varying

The COVID-19 pandemic has led to many deaths and economic disruptions across the world. Several studies have examined the effect of corresponding health risk factors in different places, but the problem of spatial heterogeneity has not been adequately addressed. The purpose of this paper was to explore how selected health risk factors are related to the pandemic infection rate within different study extents and to reveal the spatial varying characteristics of certain health risk factors. An eigenvector spatial filtering-based spatially varying coefficient model (ESF-SVC) was developed to find out how the influence of selected health risk factors varies across space and time. The ESF-SVC was able to take good control of over-fitting problems compared with ordinary least square (OLS), eigenvector spatial filtering (ESF) and geographically weighted regression (GWR) models, with a higher adjusted R2 and lower cross validation RMSE. The impact of health risk factors varied as the study extent changed: In Hubei province, only population density and wind speed showed significant spatially constant impact; while in mainland China, other factors including migration score, building density, temperature and altitude showed significant spatially varying impact. The influence of migration score was less contributive and less significant in cities around Wuhan than cities further away, while altitude showed a stronger contribution to the decrease of infection rates in high altitude cities. The temperature showed mixed correlation as time passed, with positive and negative coefficients at 2.42 °C and 8.17 °C, respectively. This study could provide a feasible path to improve the model fit by considering the problem of spatial autocorrelation and heterogeneity that exists in COVID-19 modeling. The yielding ESF-SVC coefficients could also provide an intuitive method for discovering the different impacts of influencing factors across space in large study areas. It is hoped that these findings improve public and governmental awareness of potential health risks and therefore influence epidemic control strategies.

Spatial distribution of tuberculosis and its socioeconomic influencing factors in mainland China 2013–2016

2019 ◽  
Vol 24 (9) ◽  
pp. 1104-1113
Author(s):  
Qi Wang ◽  
Liang Guo ◽  
Jing Wang ◽  
Leijie Zhang ◽  
Wanqi Zhu ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Influencing Factors ◽  
Mainland China

scholarly journals Spatial Relationship between Natural Wetlands Changes and Associated Influencing Factors in Mainland China

2020 ◽  
Vol 9 (3) ◽  
pp. 179 ◽  
Author(s):  
Ting Zhou ◽  
Anyi Niu ◽  
Zhanpeng Huang ◽  
Jiaojiao Ma ◽  
Songjun Xu
Keyword(s):  
Influencing Factors ◽  
Spatial Patterns ◽  
Water Consumption ◽  
Sustainable Management ◽  
Mainland China ◽  
Spatial Relationship ◽  
Gis Mapping ◽  
Spatial Coordination ◽  
Natural Wetlands ◽  
Wetlands Conservation

Many studies have explored the dynamic change of wetlands distribution which play an important role in wetlands conservation and its sustainable management. However, given an uneven distribution of natural wetland resources in the context of global change, little is known about the spatial relationship between natural wetlands changes and associated influencing factors in mainland China. In this study, Moran-based spatial statistics are an effective methodology to examine the spatial patterns of natural wetlands and associated influencing factors at the province level, and GIS mapping is applied to help visualize spatial patterns. Results show that 1) significant spatial agglomeration and regional differences of natural wetlands distribution have been captured by Moran’s I statistics, and the agglomeration level has increased over the past ten years; 2) Seven of the eight factors show significantly strong and positive spatial autocorrelation except for water consumption, and spatial patterns of them show significant spatial clusters or spatial outliers; 3) Spatial coordination between natural wetlands distribution and the associated influencing factors is higher in the western region than in east China and northeast China. Moreover, spatial coordination between a cultivated area or water consumption and natural wetlands distribution is weaker than that of other factors. Finally, the influences generated by neighboring provinces should not be neglected in the implementation of wetlands conservation. This study could provide a scientific basis for the policy making of wetlands conservation and sustainable management systems.

A Big Data Analysis of the Factors Influencing Movie Box Office in China

2021 ◽  
pp. 232-249
Author(s):  
Wentao Gao ◽  
Ka Man Lam ◽  
Dickson K. W. Chiu ◽  
Kevin K. W. Ho
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Influencing Factors ◽  
Mainland China ◽  
Big Data Analysis ◽  
Movie Industry ◽  
Decision Tree Analysis ◽  
Tree Analysis ◽  
Box Office ◽  
Factors Influencing

A movie's economic revenue comes mainly from the movie box office, while the influencing factors of the movie box office are complex and numerous. This research explores the influencing factors of China's commercial movie box office by analyzing the top 100 box office movies released in Mainland China between 2013-2016, with a total of 400 movies. The authors analyzed the data collected using correlation analysis and decision tree analysis using RapidMiner, respectively. Based on the analysis results, they put forward suggestions for improving the box office of the movie industry.

scholarly journals Spatial and temporal differentiation of COVID-19 epidemic spread in mainland China and its influencing factors

2020 ◽  
Vol 744 ◽  
pp. 140929 ◽  
Author(s):  
Zhixiang Xie ◽  
Yaochen Qin ◽  
Yang Li ◽  
Wei Shen ◽  
Zhicheng Zheng ◽  
...  
Keyword(s):  
Influencing Factors ◽  
Mainland China ◽  
Epidemic Spread ◽  
Temporal Differentiation

A Study On The Influencing Factors Of Tourism Demand From Mainland China To Hong Kong

2020 ◽  
Vol 45 (1) ◽  
pp. 171-191
Author(s):  
Han Liu ◽  
Wei Liu ◽  
Yonglian Wang
Keyword(s):  
Influencing Factors ◽  
Mainland China ◽  
Tourism Industry ◽  
Time Varying ◽  
Tourism Demand ◽  
Search Query ◽  
Time Varying Parameter ◽  
Vector Auto Regression ◽  
The Relationship ◽  
Auto Regression

Tourism research increasingly uses search query data to forecast demand, but the literature rarely explores the mechanisms of the factors influencing demand. A time-varying parameter factor vector auto-regression model is constructed based on Baidu Index on six aspects (dining, shopping, transportation, tours, attractions, and lodging) of tourism demand from January 2011 to March 2019. The model can quantitatively and comprehensively analyze the mechanisms of tourism demand and its six important influencing factors, and can provide suggestions for subsequent planning, construction, and services in the tourism industry. The empirical results show that the relationship between the six factors and domestic tourism demand is time-varying. Dining, attractions, and shopping have a driving effect on tourism demand, and are thus stimulative factors; transportation, tours, and lodging hinder the growth of tourism demand, and are thus baffle factors.

scholarly journals Evolving Temporal–Spatial Trends, Spatial Association, and Influencing Factors of Carbon Emissions in Mainland China: Empirical Analysis Based on Provincial Panel Data from 2006 to 2015

2018 ◽  
Vol 10 (8) ◽  
pp. 2809 ◽  
Author(s):  
Weidong Chen ◽  
Ruoyu Yang
Keyword(s):  
Panel Data ◽  
Spatial Correlation ◽  
Influencing Factors ◽  
Carbon Emissions ◽  
Industrial Structure ◽  
Spatial Association ◽  
Mainland China ◽  
Population Pressure ◽  
Spatial Trends ◽  
Investment In Fixed Assets

Based on provincial panel data from 2005 to 2016, this paper analyzes evolving temporal–spatial trends, spatial correlation and influencing factors of carbon emissions in China. The results show that there is a great heterogeneity in the evolving temporal–spatial trends of carbon emissions among provinces and regions in China, with the heterogeneity in eastern provinces most obvious. At the same time, there exists significant spatial correlation and agglomeration of carbon emissions in 30 provinces. It is found that the distribution characteristics of carbon emissions are affected by various economic and social factors based on the extended STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model. Population pressure, affluence, energy intensity, industrial structure, urbanization level and investment in fixed assets can significantly promote the increase of carbon emissions. The technological level and government environmental supervision have significant inhibitory effects on carbon emissions, but foreign direct investment (FDI) has no significant impact. Therefore, it is necessary to strengthen environmental supervision and upgrade technology level to promote carbon emission reduction.

Factors Influencing Chinese Professional Caregivers’ Bereavement Experiences After Patient Deaths: A Secondary Qualitative Study

2020 ◽  
pp. 003022282094898
Author(s):  
Lina Du ◽  
Chuqian Chen ◽  
Changsong Yang
Keyword(s):  
Influencing Factors ◽  
Lived Experiences ◽  
Qualitative Data ◽  
Mainland China ◽  
Structured Interview ◽  
Professional Lives ◽  
Cultural Backgrounds ◽  
Factors Influencing ◽  
Bereaved Family ◽  
Chinese Physicians

This study aims to identify the factors that influence Chinese professional caregivers’ bereavement experiences after patient deaths. Through a content analysis, the study reanalyzed the qualitative data initially collected to understand the lived experiences of professional bereavement in Mainland China. Specifically, the study assessed semi-structured interview transcripts conducted with 24 Chinese physicians and nurses and generated 15 open codes, reflecting the influencing factors. These were further categorized into four themes: dying and death conditions, professional caregivers' characteristics, professional caregivers’ involvement, and the bereaved family. The results revealed that professional bereavement experiences and the unveiled factors relate to both the personal and professional lives of the interviewees. Overall, the health care system and cultural backgrounds should be listed as influencing factors for professional bereavement experiences in addition to the aforementioned four.

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