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School intervention to early detection of mental disorders among 15-19 aged, in Sri Lanka

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dc.contributor.advisor Premaratne S Tharika TADKA 2019 2019 2019
dc.description.abstract Presently students that are in the age of 15 to 19, all over the world are experiencing mental disorders. These disorders have become the prominent cause of disability in young people. However, when accurately diagnosis of mental disorders at an early stage improves the quality of life of students and it also helps to avoid problematical events at the later stage. School Teachers, Counselors, and Mental health-oriented organizations, embedded within schools can perform various kinds of tasks for child‟s mental care due to those it improves physical health, mental health and educational attainment for adolescents. To strengthen these efforts, optimum child development, a reconfiguration of School education system and mental health systems to aid implementation of the evidence-based system might be required. Having integrative strategies like combine classroom-level and student-level interventions is necessary. A lot of Ethical, social and scientific justifications exist to integrate the mental health system and Education System. In Sri Lankan education system, Class Teacher and Student have a very close relationship, hence if coupled with the use of evidence-based detection pattern and teachers‟ hands-on experience to detect and identify the students‟ behavioral changes are the aid to early detection of Mental Disorders. This study focuses on detecting fundamental mental health problems, like Depression and Anxiety Symptoms, using Data mining techniques. en_US
dc.language.iso en en_US
dc.subject INFORMATION TECHNOLOGY-Dissertations en_US
dc.subject DATA MINING en_US
dc.subject MENTAL ILLNESS-Detection en_US
dc.subject ANXIETY en_US
dc.title School intervention to early detection of mental disorders among 15-19 aged, in Sri Lanka en_US
dc.type Thesis-Abstract en_US
dc.identifier.faculty IT en_US MSc in Information Technology en_US
dc.identifier.department Department of Information Technology en_US 2019
dc.identifier.accno TH3900 en_US

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