Economic, Social, Medical, Work Injury, and Environmental Efficiency Assessments

Fang, Zhong; Chiu, Yung‐ho; Lin, Tai Yu; Chang, Tzu‐Han

doi:10.1177/0046958020972211

Cited by 2 publications

(3 citation statements)

References 41 publications

(55 reference statements)

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“…Work efficiency monitoring methods aim to assess and optimize the productivity and effectiveness of individuals, teams, or organizations in accomplishing their tasks and goals. These methods encompass various techniques, tools, and approaches to gather data, analyze performance, and provide insights for improvement [11][12][13][14]. Project management allows teams to organize and track tasks, assign responsibilities, set deadlines, and monitor progress, providing visibility into project status and identify bottlenecks or areas needing improvement [15].…”

Section: ⅱ Related Workmentioning

confidence: 99%

Machine Learning-Based Work Efficiency Monitoring Method for Student Supervisor

2023

J Multimed Inf Syst

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As student supervisor workloads increase, ensuring efficient support is challenging. A novel machine learning method using clustering analysis is proposed to gain insights into time allocation and identify optimization opportunities. Specifically, a covariance distance measure analysis approach is presented for characterizing the closeness of data items in multi-dimensional cluster analysis through efficient distance measuring. The membership degree of each sample point is first determined concerning class characteristics through the fuzzy c-means approach, which involves first assigning weights to data items. Then, the membership degree is used to determine the degree of dissimilarity between the samples. Finally, the covariance distance measurement between sample points is used as the second optimization criterion to distinguish between data types, and the optimal solution is calculated iteratively by alternating fixed variables to optimize the clustering metric and the distance measurement learning parameters and produce more accurate clustering results. The experimental findings on datasets demonstrate that the proposed approach outperforms benchmarks in terms of clustering accuracy. Therefore, the high clustering accuracy empowers the student supervisor to make informed decisions about time allocation, allowing them to optimize their efforts and resources.

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Section: ⅱ Related Workmentioning

confidence: 99%

Machine Learning-Based Work Efficiency Monitoring Method for Student Supervisor

2023

J Multimed Inf Syst

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“…The previous literature has focused on medical insurance and endowment insurance when discussing social insurance, but few studies have included the most relevant work-related injury insurance for young and middle-aged labor in a social security system. Fang et al [ 37 ] discussed the impact of air pollution on the efficiency of the social security system. However, whether this impact will change due to differences in local economies is critical and unclear.…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, whether this impact will change due to differences in local economies is critical and unclear. Therefore, on the basis of Fang et al [ 37 ], this present study divided China’s provinces into high-income regions and middle-income regions and developed a meta-two-stage undesirable dynamic DDF under an exogenous model in order to explore the local economy’s impact on the model. At the same time, this study added bad outputs in the first and second stages to reflect the shortcomings of the social security system.…”

Section: Literature Reviewmentioning

confidence: 99%

Air Pollution’s Impact on the Economic, Social, Medical, and Industrial Injury Environments in China

Fang

Lin

et al. 2021

Healthcare

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In this era of rapid economic development, it is inevitable that economic activities eventually cause serious damage to the environment’s air quality, making it the focus of global public health. If the treatment efficiency of medical accidents can be improved, then this can significantly stabilize society and improve production efficiency. Past research has mainly focused on work safety and health issues, seldom discussing economic, social, medical, and environmental pollution issues together, and, most generally, adopted static methods that fail to recognize how air pollution affects the overall economy, society, medical care, and external environment. In order to more deeply understand the changes among social, economic activities, and environmental issues due to air pollution, this study proposes a meta-two-stage undesirable dynamic DDF (Direction Distance Function) that, under an exogenous model, divides the 30 provinces of China into high-income regions and middle-income regions and explores the economic, social, medical, and environmental efficiencies between the two areas to resolve the lack of related static analyses. The empirical results are as follows. (1) The AQI (air quality index) significantly impacts the efficiency of medical injuries in various regions. (2) When the AQI is considered, the medical insurance expenditure efficiency score value of high-income areas is lower than the value without the AQI. (3) When the AQI is considered, the efficiency value of the number of work injury insurance benefits in the middle-income area is lower than the efficiency value without the AQI.

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Economic, Social, Medical, Work Injury, and Environmental Efficiency Assessments

Cited by 2 publications

References 41 publications

Machine Learning-Based Work Efficiency Monitoring Method for Student Supervisor

Machine Learning-Based Work Efficiency Monitoring Method for Student Supervisor

Air Pollution’s Impact on the Economic, Social, Medical, and Industrial Injury Environments in China

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