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Updated: Aug 11, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Early detection of depression and associated risk factors in adults with mild/moderate intellectual disability
Jane A McGillivray1, Marita P McCabe
1School of Psychology, Deakin University, 221 Burwood Highway, Burwood, Vic. 3125, Australia. mcgill@deakin.edu.au
Abstract:
The aim of this study was to determine the presentation and risk factors for depression in adults with mild/moderate intellectual disability (ID). A sample of 151 adults (83 males and 68 females) participated in a semi-structured interview. According to results on the Beck Depression Inventory II, 39.1% of participants evinced symptoms of depression (2 severe, 14 moderate, and 43 mild). Sadness, self-criticism, loss of energy, crying, and tiredness appeared to be the most frequent indicators of depression or risk for depression. A significant difference was found between individuals with and without symptoms of depression on levels of automatic negative thoughts, downward social comparison and self-esteem. Automatic negative thoughts, quality and frequency of social support, self-esteem, and disruptive life events significantly predicted depression scores in people with mild/moderate ID, accounting for 58.1% of the variance.
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