Background: Further strategies are needed to deal with the high losses to suicide. New modalities should be explored within the context of suicide prevention. Aim: The aim of the study was to evaluate participants’ e...Background: Further strategies are needed to deal with the high losses to suicide. New modalities should be explored within the context of suicide prevention. Aim: The aim of the study was to evaluate participants’ experiences of a web based program for mental health care staff, including its potential clinical relevance. Methods: Nineteen participants participated in five focus groups. Data was analyzed using content analysis. Results: The analysis showed participants’ experiences of the program’s contents and format (“Web Based Modules”, “Discussion Groups”) and practical value (“Clinical Relevance and Use”, “Effects on Communication and Climate”). Conclusions: The program partly increased awareness about risk factors and the importance of inquiring about suicide ideation/plans and documenting suicide assessments. Experiences of the clinical value were varying and may be increased through potential enhancements.展开更多
Inmates in jails and prisons are a high-risk group for suicide. Often, legal claims of medical negligence and §1983 based on deliberate indifference by the correctional officials follow. Unique stable and dynamic...Inmates in jails and prisons are a high-risk group for suicide. Often, legal claims of medical negligence and §1983 based on deliberate indifference by the correctional officials follow. Unique stable and dynamic factors and circumstances and their interaction explain most suicides. A systematic suicide screening using a well-designed suicide screening questionnaire that captures known risk factors will identify most inmates at risk if adequately used. Identification and subsequent mental health assessment set the stage for preventive intervention, monitoring, and treatment of the inmates. A multidimensional suicide prevention program saves not only lives but also the best defense against a liability lawsuit.展开更多
The phenomenon of teenage campus suicide has become the focus of attention of parents,schools and the society.The causes behind it are extremely complicated,and the root cause is psychological and spiritual problems.H...The phenomenon of teenage campus suicide has become the focus of attention of parents,schools and the society.The causes behind it are extremely complicated,and the root cause is psychological and spiritual problems.However,one’s negative psychology is often hidden,and it is difficult to detect and effectively intervene before the tragedy.How to effectively identify students with suicidal tendencies in order to prevent tragedies has aroused extensive research and discussion among the government,academia and the public.Through investigation and research,it is found that the current popular computer cutting-edge technologies such as artificial intelligence and computer vision can be well used for human emotion recognition and behavior prediction,and put into use in schools as a mental health auxiliary diagnosis and treatment system,thus effectively reducing the suicide rate on campus.The scenario assumes that machine learning can be used to deduce the risk of psychological problems in human samples by analyzing the frequency of negative emotions in facial expressions.Based on this,this paper proposes an effective solution for campus suicide prediction,and designs a set of auxiliary diagnosis and treatment system based on campus monitoring network system for suicide behavior prediction and student mental health analysis.Through preliminary experimental analysis and verification,the suicide psychological auxiliary diagnosis and treatment system has achieved good results in face recognition success rate,emotion recognition success rate and behavior prediction success rate.With the input of more experimental data and the increase of self-training time,the prediction system will perform better.展开更多
The phenomenon of teenage campus suicide has become the focus of attention of parents,schools and the society.The causes behind it are extremely complicated,and the root cause is psychological and spiritual problems.H...The phenomenon of teenage campus suicide has become the focus of attention of parents,schools and the society.The causes behind it are extremely complicated,and the root cause is psychological and spiritual problems.However,one's negative psychology is often hidden,and it is difficult to detect and effectively intervene before the tragedy.How to effectively identify students with suicidal tendencies in order to prevent tragedies has aroused extensive research and discussion among the government,academia and the public.Through investigation and research,it is found that the current popular computer cutting-edge technologies such as artificial intelligence and computer vision can be well used for human emotion recognition and behavior prediction,and put into use in schools as a mental health auxiliary diagnosis and treatment system,thus effectively reducing the suicide rate on campus.The scenario assumes that machine learning can be used to deduce the risk of psychological problems in human samples by analyzing the frequency of negative emotions in facial expressions.Based on this,this paper proposes an effective solution for campus suicide prediction,and designs a set of auxiliary diagnosis and treatment system based on campus monitoring network system for suicide behavior prediction and student mental health analysis.Through preliminary experimental analysis and verification,the suicide psychological auxiliary diagnosis and treatment system has achieved good results in face recognition success rate,emotion recognition success rate and behavior prediction success rate.With the input of more experimental data and the increase of self-training time,the prediction system will perform better.展开更多
文摘Background: Further strategies are needed to deal with the high losses to suicide. New modalities should be explored within the context of suicide prevention. Aim: The aim of the study was to evaluate participants’ experiences of a web based program for mental health care staff, including its potential clinical relevance. Methods: Nineteen participants participated in five focus groups. Data was analyzed using content analysis. Results: The analysis showed participants’ experiences of the program’s contents and format (“Web Based Modules”, “Discussion Groups”) and practical value (“Clinical Relevance and Use”, “Effects on Communication and Climate”). Conclusions: The program partly increased awareness about risk factors and the importance of inquiring about suicide ideation/plans and documenting suicide assessments. Experiences of the clinical value were varying and may be increased through potential enhancements.
文摘Inmates in jails and prisons are a high-risk group for suicide. Often, legal claims of medical negligence and §1983 based on deliberate indifference by the correctional officials follow. Unique stable and dynamic factors and circumstances and their interaction explain most suicides. A systematic suicide screening using a well-designed suicide screening questionnaire that captures known risk factors will identify most inmates at risk if adequately used. Identification and subsequent mental health assessment set the stage for preventive intervention, monitoring, and treatment of the inmates. A multidimensional suicide prevention program saves not only lives but also the best defense against a liability lawsuit.
文摘The phenomenon of teenage campus suicide has become the focus of attention of parents,schools and the society.The causes behind it are extremely complicated,and the root cause is psychological and spiritual problems.However,one’s negative psychology is often hidden,and it is difficult to detect and effectively intervene before the tragedy.How to effectively identify students with suicidal tendencies in order to prevent tragedies has aroused extensive research and discussion among the government,academia and the public.Through investigation and research,it is found that the current popular computer cutting-edge technologies such as artificial intelligence and computer vision can be well used for human emotion recognition and behavior prediction,and put into use in schools as a mental health auxiliary diagnosis and treatment system,thus effectively reducing the suicide rate on campus.The scenario assumes that machine learning can be used to deduce the risk of psychological problems in human samples by analyzing the frequency of negative emotions in facial expressions.Based on this,this paper proposes an effective solution for campus suicide prediction,and designs a set of auxiliary diagnosis and treatment system based on campus monitoring network system for suicide behavior prediction and student mental health analysis.Through preliminary experimental analysis and verification,the suicide psychological auxiliary diagnosis and treatment system has achieved good results in face recognition success rate,emotion recognition success rate and behavior prediction success rate.With the input of more experimental data and the increase of self-training time,the prediction system will perform better.
文摘The phenomenon of teenage campus suicide has become the focus of attention of parents,schools and the society.The causes behind it are extremely complicated,and the root cause is psychological and spiritual problems.However,one's negative psychology is often hidden,and it is difficult to detect and effectively intervene before the tragedy.How to effectively identify students with suicidal tendencies in order to prevent tragedies has aroused extensive research and discussion among the government,academia and the public.Through investigation and research,it is found that the current popular computer cutting-edge technologies such as artificial intelligence and computer vision can be well used for human emotion recognition and behavior prediction,and put into use in schools as a mental health auxiliary diagnosis and treatment system,thus effectively reducing the suicide rate on campus.The scenario assumes that machine learning can be used to deduce the risk of psychological problems in human samples by analyzing the frequency of negative emotions in facial expressions.Based on this,this paper proposes an effective solution for campus suicide prediction,and designs a set of auxiliary diagnosis and treatment system based on campus monitoring network system for suicide behavior prediction and student mental health analysis.Through preliminary experimental analysis and verification,the suicide psychological auxiliary diagnosis and treatment system has achieved good results in face recognition success rate,emotion recognition success rate and behavior prediction success rate.With the input of more experimental data and the increase of self-training time,the prediction system will perform better.
