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    Analysis of affective disorders and its association with resilience and other socio demographic factors among primary school children in Kenya using binary logistic regression

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    Date
    2014
    Author
    Musyimi, Christine W
    Type
    Thesis; en_US
    Language
    en
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    Abstract
    One out of every four people in Kenya is depressed. However, resilience conceptualized as an individual’s ability to cope with adversities has been shown to be one of the most protective factors against affective disorders as well as other mental disorders. This study thus aimed at exploring the relationship between affective disorders and resilience as well as other significant predictors of affective disorder among school children in Kenya. Analysis was based on secondary data from a cross sectional study that involved administration of the Youth Self Report (YSR) and socio demographic questionnaire to 23 sampled schools in Kenya. 2261 children and adolescents between standard five through seven were included in our analysis. A total of 2261 children with a mean age 12.66 years (range 10 to 18 years) were included in our analysis. 14% of school children had affective problems with 12.8% having more than one common psychiatric disorder. Using binary logistic regression, male gender, mother's employment, being older for a particular grade, residing from a peri-urban area and having divorced or separated parents increased the likelihood of having an affective disorder. A key finding was that high resilience trait was a protective factor for affective disorders. In conclusion, affective disorders are common among children and adolescents. Findings from this study provide evidence that resilience is a significant negative predictor of affective disorder. Therefore, fostering resilience at a younger age especially in schools and at home is crucial in preventing and reducing affective disorders and psychiatric co morbidities in order to prevent any detrimental effects as children and adolescents progress to adulthood.
    URI
    http://hdl.handle.net/11295/76718
    Publisher
    University of Nairobi
    Description
    Thesis Master of Science in Medical Statistics
    Collections
    • Faculty of Health Sciences (FHS) [4559]

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