Suchergebnisse

Purpose
Energy conservation and environmental protection industry (ECEPI) is a strategic choice to promote energy conservation and emission reduction, develop green economy and circular economy. However, China's ECEPI is still in the stage of rapid development and the overall scale is relatively small, what development periods have the ECEPI experienced? This study aims to contribute to a better understanding of collaborative innovation evolution based on social network analysis from the perspective of multi-dimensional proximity.


Design/methodology/approach
Methodologically, this study uses social network analysis method to explore the co-evolution of multidimensional collaboration networks. It divides China's ECEPI into four periods based on national policies from 2001 to 2020. This contribution constructs collaborative innovation networks from geographical, technological and organizational proximity.


Findings
The results show that the collaborative innovation network was initially formed in the central region of China, gradually expanded to neighboring cities and the core positions of Beijing, Jiangsu and Guangdong have been continuously consolidated. C02F has been the core of the collaboration networks, and the research focus has gradually shifted from the treatment of wastewater, sewage or sludge to the separation field. Enterprises always occupy a dominant position in the collaboration networks.


Originality/value
This research investigates the dynamic evolution process of collaborative innovation network in China's ECEPI from the perspective of multidimensional proximity, explores the community structure, important nodes and multidimensional proximity features in the network, expands the research perspective on evolution characteristics of innovative network and the research field of social network analysis. Theoretically, this study enriches collaborative innovation theory, social network theory and multi-dimensional proximity theory.

AbstractBackgroundInterprofessional collaboration (IPC) is a key contributor to the health‐care organizational culture of wellness and health‐care provider (HCP) morale.ObjectiveThe purpose of this study is examining the impact of IPC on their work stress among HCPs in China and comparing the differences in associations between IPC and work stress between Chinese physicians and nurses.MethodsWith a survey research design, 1036 HCPs were electronically recruited. Five multiple linear regression models were developed to examine the association between IPC and work stress among general HCPs, physicians, and nurses.ResultsIPC can significantly reduce work stress among Chinese HCPs. Regarding IPC, achieving mutually satisfactory solutions, having a clear understanding of boundaries, and a sense of trust are negatively associated with HCPs' work stress, but team reflective revision was positively associated with HCPs' work stress during the 2019 Coronavirus Disease (COVID‐19) pandemic. However, IPC factors are associated with work stress differently between physicians and nurses in China.ConclusionOur findings have important implications for practice, research, and policy.

Abstract
Data analyses using artificial intelligence (AI) have not gained popularity in social work as much as other disciplines. To demonstrate its use, this study focused on Chinese older adults with neurodegenerative diseases (NDs) to (i) develop a prediction model using decision tree model to identify factors associated with depression and (ii) compare the prediction performance of decision tree model with that of logistic regression analysis. Decision tree model processing involved four stages: data collection, data preparation, model development, and result evaluation. An algorithm named Classification and Regression Trees (CARTs) was utilised to grow the decision tree by Python 3.7.1. The performance evaluation was based on accuracy, sensitivity, specificity and Goodness index (G). Seven factors grew the decision tree, including Instrumental Activities of Daily Living (IADLs), Mini-Mental State Examination (MMSE), Health status, Activity of Daily Living (ADL), Gender, Self-rated health change and Age. When compared to logistic regression, the decision tree model had a much better performance in depression prediction. Researchers, practitioners and policymakers need to focus on ways to decrease the vulnerability of depression in Chinese older adults with NDs. Also, the decision tree model can be applied as a referral to other physical or mental diseases prediction and analysis.