Early detection of handicapping conditions in infancy and early childhood: Toward a multivariate model |
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Authors: | Thomas T. Kochanek |
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Affiliation: | 1. Experimental Medicine, Department of Medicine, University of British Columbia, Vancouver, BC, Canada;2. Blizard Institute, Queen Mary University of London, London, U.K;3. University of Zimbabwe College of Health Sciences, Harare, Zimbabwe;4. Zvitambo Institute for Maternal and Child Health Research, Harare, Zimbabwe.;5. School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada;6. British Columbia Centre for Disease Control (BCCDC), Vancouver, BC, Canada;1. Discipline of Dental Anaesthesia, Faculty of Dentistry, University of Toronto, Toronto, ON, Canada;2. Department of Clinical Sciences (Pharmacology & Preventive Dentistry), Faculty of Dentistry, University of Toronto, Toronto, ON, Canada |
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Abstract: | The major purpose of this study was to relate to infancy the occurence of significant learning or behavioral problems in adolescence. Original participants in the National Collaborative Perinatal Project in Rhode Island, who were also judged as handicapped after school entry, comprised the sample for this study. Environmental factors and child performance data collected at birth and at 4, 8, and 12 months of age were examined in terms of their power to predict handicapping conditions in the adolescent. Our results indicated that maternal education was a more accurate predictor of later learning and behavioral competency than the child's own developmental status up to 12 months of age. Furthermore, combining child-centered data and maternal education did not enhance predictive accuracy beyond that of maternal education considered in isolation. These findings underscore the critical role of molar environmental variables as antecedents of school failure, and suggest that models for screening in the first year of life and of multidisciplinary team diagnosis for handicapped children should include both environmental and child-focused dimensions to minimize overall classification error. |
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