Social-Emotional Competency(SEC),regarded as a critical psychological resource for individuals to adapt to social environments,is an effective protective factor for students’mental health,impacting their future succe...Social-Emotional Competency(SEC),regarded as a critical psychological resource for individuals to adapt to social environments,is an effective protective factor for students’mental health,impacting their future success and well-being.Analyzing the impact of SEC on university students’mental health can offer valuable insights for nurturing talents with healthy psychological and physical development.Based on data from two large-scale surveys of Chinese university students,this study designed two comprehensive Multiple Mediation Models involving SEC,stress,coping strategies,and stress reaction to explore the pathway of emotion nurturing mentality.Study 1 utilized a parallel mediation model to examine the relationships between SEC,academic stress,interpersonal stress,and stress reactions.The results indicated that SEC negatively predicted academic stress,interpersonal stress,and stress reactions.Additionally,academic and interpersonal stress mediated the relationships between SEC and stress reactions in parallel.Extending these findings,Study 2 further investigated the role of coping strategies.By constructing a multiple-chain mediation model,it examined the predictive relationships among SEC,academic stress,interpersonal stress,three types of coping strategies,and stress reactions.The findings indicated that SEC negatively predicted stress,problemavoiding strategy,and stress reactions,while positively predicting problem-solving and assistant-seeking strategies.Furthermore,the two stress types and three coping strategies significantly mediated the relationship between SEC and stress reactions.This indicated that higher SEC was associated with reduced stress and more adaptive coping strategies and subsequently contributed to more favorable stress reactions.This research explored the impact of university students’SEC on mental health and its relational mechanisms,aiming to provide theoretical reference and practical insights for future efforts in cultivating SEC among university students to adjust academic and in展开更多
Objective To discuss how to use social media data for post-marketing drug safety monitoring in China as soon as possible by systematically combing the text mining applications,and to provide new ideas and methods for ...Objective To discuss how to use social media data for post-marketing drug safety monitoring in China as soon as possible by systematically combing the text mining applications,and to provide new ideas and methods for pharmacovigilance.Methods Relevant domestic and foreign literature was used to explore text classification based on machine learning,text mining based on deep learning(neural networks)and adverse drug reaction(ADR)terminology.Results and Conclusion Text classification based on traditional machine learning mainly include support vector machine(SVM)algorithm,naive Bayesian(NB)classifier,decision tree,hidden Markov model(HMM)and bidirectional en-coder representations from transformers(BERT).The main neural network text mining based on deep learning are convolution neural network(CNN),recurrent neural network(RNN)and long short-term memory(LSTM).ADR terminology standardization tools mainly include“Medical Dictionary for Regulatory Activities”(MedDRA),“WHODrug”and“Systematized Nomenclature of Medicine-Clinical Terms”(SNOMED CT).展开更多
基金Shandong Soft Science Project—Shandong Higher Education Top-Notch Talent Cultivation in Basic Disciplines from the Perspective of Social Emotional Learning(Project Number:2023RKY05001).
文摘Social-Emotional Competency(SEC),regarded as a critical psychological resource for individuals to adapt to social environments,is an effective protective factor for students’mental health,impacting their future success and well-being.Analyzing the impact of SEC on university students’mental health can offer valuable insights for nurturing talents with healthy psychological and physical development.Based on data from two large-scale surveys of Chinese university students,this study designed two comprehensive Multiple Mediation Models involving SEC,stress,coping strategies,and stress reaction to explore the pathway of emotion nurturing mentality.Study 1 utilized a parallel mediation model to examine the relationships between SEC,academic stress,interpersonal stress,and stress reactions.The results indicated that SEC negatively predicted academic stress,interpersonal stress,and stress reactions.Additionally,academic and interpersonal stress mediated the relationships between SEC and stress reactions in parallel.Extending these findings,Study 2 further investigated the role of coping strategies.By constructing a multiple-chain mediation model,it examined the predictive relationships among SEC,academic stress,interpersonal stress,three types of coping strategies,and stress reactions.The findings indicated that SEC negatively predicted stress,problemavoiding strategy,and stress reactions,while positively predicting problem-solving and assistant-seeking strategies.Furthermore,the two stress types and three coping strategies significantly mediated the relationship between SEC and stress reactions.This indicated that higher SEC was associated with reduced stress and more adaptive coping strategies and subsequently contributed to more favorable stress reactions.This research explored the impact of university students’SEC on mental health and its relational mechanisms,aiming to provide theoretical reference and practical insights for future efforts in cultivating SEC among university students to adjust academic and in
文摘Objective To discuss how to use social media data for post-marketing drug safety monitoring in China as soon as possible by systematically combing the text mining applications,and to provide new ideas and methods for pharmacovigilance.Methods Relevant domestic and foreign literature was used to explore text classification based on machine learning,text mining based on deep learning(neural networks)and adverse drug reaction(ADR)terminology.Results and Conclusion Text classification based on traditional machine learning mainly include support vector machine(SVM)algorithm,naive Bayesian(NB)classifier,decision tree,hidden Markov model(HMM)and bidirectional en-coder representations from transformers(BERT).The main neural network text mining based on deep learning are convolution neural network(CNN),recurrent neural network(RNN)and long short-term memory(LSTM).ADR terminology standardization tools mainly include“Medical Dictionary for Regulatory Activities”(MedDRA),“WHODrug”and“Systematized Nomenclature of Medicine-Clinical Terms”(SNOMED CT).
