Community environment and physical activity influence on rural residents’ mental health in the COVID-19 containment

Jia, Ziyu; Gao, Mengyao; Zhang, Zican; Xu, Shijia; Pan, Ziyan; Cheng, Zhengyu; Han, Haoqing; Lv, Xiaohe; Zhou, Zucheng

doi:10.1080/09603123.2022.2029828

Cited by 3 publications

(2 citation statements)

References 33 publications

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“…First, additional in uences of certain factors were not considered in this research, such as whether social engagement may moderate the effects of solitary living on the mental health of the elderly. Moreover, due to the limitations of the database, some other factors may have remained uncontrolled in this research, such as social support and community environment [34,35]. Finally, the CES-D is a self-report scale, which was susceptible to deviations in the evaluation of seniors' depression levels.…”

Section: Discussionmentioning

confidence: 99%

Does Living Alone Jeopardize Seniors’ Mental Health? Evidence from China

Wang,

Sun

2024

Preprint

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Introduction: Solitary living has gradually gained popularity in China. From a spatial perspective, this trend weakens the emotional connections between parents and children, a fundamental concept in Confucianism. This has raised concerns about the mental health of older adults who live alone. Despite prior research having evaluated the influence of solitary living on seniors’ mental health in different cultures, there remains controversy about these conclusions. Methods: This article adopted fixed effect model to analyze three sets of data from the China Health and Retirement Longitudinal Study based on different family cultures reflected by living arrangements and mental health. Results: The results indicate that living alone reduces seniors’ depression levels by 0.322 (P< 0.01). The positive effects generated by solitary living on seniors’ mental health originate from living close to their children. This living style effectively balances the need for individual independence and the demand for maintaining close family relationships, reconciling intergenerational family cultures.Differences in family cultures are also identified between rural and urban areas. This study indicates that living alone is good for the mental health of seniors in rural areas; however, no significant effects were found for older adults in urban areas. This difference is likely because living distantly from their children has increased the depression levels of seniors in urban areas. Discussion: As living close to their children positively impacts seniors’ mental health, policymakers are advised to focus on enhancing local government’s capabilities to absorb employment. Special emphasis should be placed on incentivizing migrant workers in urban areas to consider returning to their hometowns to initiate entrepreneurial ventures or seek employment opportunities.

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Section: Discussionmentioning

confidence: 99%

Does Living Alone Jeopardize Seniors’ Mental Health? Evidence from China

Wang,

Sun

2024

Preprint

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“…In recent years, the analysis and mining of time series data have been gradually applied to many fields. Natural language processing is a typical application [23][24][25][26]. In the research of many scholars in the scope of natural language processing, the text sentiment analysis task has two important steps.…”

Section: Relevant Theoretical Basis and Technical Introductionmentioning

confidence: 99%

Research on Sentiment Analysis Model of Short Text Based on Deep Learning

Zhou

2022

Scientific Programming

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With the wide application of the Internet and the rapid development of network technology, microblogs and online shopping platforms are playing an increasingly important role in people’s daily life, learning, and communication. The length of these information texts is usually relatively short, and the grammatical structure is not standardized, but it contains rich emotional tendencies of users. The features used by custumal machinery schooling methods are too sparse on the vector space model and lack the semantic information of short texts, which cannot well identify the semantic features and potential emotional features of short texts. In response to the above problems, this paper proposes a bidirectional long-term and short-term memory network model based on emotional multichannel, combining the attention mechanism and convolutional neural network features in deep learning and learning the short text by combining shallow learning and deep learning. The semantic information and potential emotional information of the short text can be improved to promote the effective expression of short-text emotional features and improve the short-text emotional classification effect. Finally, this paper compares the above models on multidomain classification data sets such as NLPIR and NLPCC2014. The accuracy and F1 value of the model proposed in this paper have achieved good improvement in the field of short-text sentiment analysis.

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Improved GCN Framework for Human Motion Recognition

Zhou

Jiang

2022

Scientific Programming

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Human recognition models based on spatial-temporal graph convolutional neural networks have been gradually developed, and we present an improved spatial-temporal graph convolutional neural network to solve the problems of the high number of parameters and low accuracy of this type of model. The method mainly draws on the inception structure. First, the tensor rotation is added to the graph convolution layer to realize the conversion between graph node dimension and channel dimension and enhance the model’s ability to capture global information for small-scale tasks. Then the inception temporal convolution layer is added to build a multiscale temporal convolution filter to perceive temporal information under different time domains hierarchically from 4-time dimensions. It overcomes the shortcomings of temporal graph convolutional networks in the field of joint relevance of hidden layers and compensates for the information omission of small-scale graph tasks. It also limits the volume of parameters, decreases the arithmetic power, and speeds up the computation. In our experiments, we verify our model on the public dataset NTU RGB + D. Our method reduces the number of the model parameters by 50% and achieves an accuracy of 90% in the CS evaluation system and 94% in the CV evaluation system. The results show that our method not only has high recognition accuracy and good robustness in human behavior recognition applications but also has a small number of model parameters, which can effectively reduce the computational cost.

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Community environment and physical activity influence on rural residents’ mental health in the COVID-19 containment

Cited by 3 publications

References 33 publications

Does Living Alone Jeopardize Seniors’ Mental Health? Evidence from China

Does Living Alone Jeopardize Seniors’ Mental Health? Evidence from China

Research on Sentiment Analysis Model of Short Text Based on Deep Learning

Improved GCN Framework for Human Motion Recognition

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