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Self-assessment of functional ability in schizophrenia: milestone achievement and its relationship to accuracy of self-evaluation.

ABSTRACT: Between 50% and 80% of patients with schizophrenia do not believe they have any illness, and their self-assessment of cognitive impairments and functional abilities is also impaired compared to other information, including informant reports and scores on performance-based ability measures. The present article explores self-assessment accuracy in reference to real world functioning as measured by milestone achievement such as employment and independent living. Our sample included 195 people with schizophrenia examined with a performance-based assessment of neurocognitive abilities and functional capacity. We compared patient self-assessments across achievement of milestones, using patient performance on cognitive and functional capacity measures as a reference point. Performance on measures of functional capacity and cognition was better in people who had achieved employment and residential milestones. Patients with current employment and independence in residence rated themselves as more capable than those who were currently unemployed or not independent. However, individuals who had never had a job rated themselves at least as capable as those who had been previously employed. These data suggest that lifetime failure to achieve functional milestones is associated with overestimation of abilities. As many patients with schizophrenia never achieve milestones, their self-assessment may be overly optimistic as a result.

SUBMITTER: Gould F

PROVIDER: S-EPMC3640769 | biostudies-literature | 2013 May

REPOSITORIES: biostudies-literature

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Self-assessment of functional ability in schizophrenia: milestone achievement and its relationship to accuracy of self-evaluation.

Gould Felicia F Sabbag Samir S Durand Dante D Patterson Thomas L TL Harvey Philip D PD

Psychiatry research 20130326 1-2

Between 50% and 80% of patients with schizophrenia do not believe they have any illness, and their self-assessment of cognitive impairments and functional abilities is also impaired compared to other information, including informant reports and scores on performance-based ability measures. The present article explores self-assessment accuracy in reference to real world functioning as measured by milestone achievement such as employment and independent living. Our sample included 195 people with ...[more]

PMID: 23537844

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Project description:BackgroundCare for children with autism spectrum disorder (ASD) can be challenging for families and medical care systems. This is especially true in low- and- middle-income countries such as Bangladesh. To improve family-practitioner communication and developmental monitoring of children with ASD, mCARE (Mobile-Based Care for Children with Autism Spectrum Disorder Using Remote Experience Sampling Method) was developed. Within this study, mCARE was used to track child milestone achievement and family sociodemographic assets to inform mCARE feasibility/scalability and family asset-informed practitioner recommendations.ObjectiveThe objectives of this paper are threefold. First, it documents how mCARE can be used to monitor child milestone achievement. Second, it demonstrates how advanced machine learning models can inform our understanding of milestone achievement in children with ASD. Third, it describes family/child sociodemographic factors that are associated with earlier milestone achievement in children with ASD (across 5 machine learning models).MethodsUsing mCARE-collected data, this study assessed milestone achievement in 300 children with ASD from Bangladesh. In this study, we used 4 supervised machine learning algorithms (decision tree, logistic regression, K-nearest neighbor [KNN], and artificial neural network [ANN]) and 1 unsupervised machine learning algorithm (K-means clustering) to build models of milestone achievement based on family/child sociodemographic details. For analyses, the sample was randomly divided in half to train the machine learning models and then their accuracy was estimated based on the other half of the sample. Each model was specified for the following milestones: Brushes teeth, Asks to use the toilet, Urinates in the toilet or potty, and Buttons large buttons.ResultsThis study aimed to find a suitable machine learning algorithm for milestone prediction/achievement for children with ASD using family/child sociodemographic characteristics. For Brushes teeth, the 3 supervised machine learning models met or exceeded an accuracy of 95% with logistic regression, KNN, and ANN as the most robust sociodemographic predictors. For Asks to use toilet, 84.00% accuracy was achieved with the KNN and ANN models. For these models, the family sociodemographic predictors of "family expenditure" and "parents' age" accounted for most of the model variability. The last 2 parameters, Urinates in toilet or potty and Buttons large buttons, had an accuracy of 91.00% and 76.00%, respectively, in ANN. Overall, the ANN had a higher accuracy (above ~80% on average) among the other algorithms for all the parameters. Across the models and milestones, "family expenditure," "family size/type," "living places," and "parent's age and occupation" were the most influential family/child sociodemographic factors.ConclusionsmCARE was successfully deployed in a low- and middle-income country (ie, Bangladesh), providing parents and care practitioners a mechanism to share detailed information on child milestones achievement. Using advanced modeling techniques this study demonstrates how family/child sociodemographic elements can inform child milestone achievement. Specifically, families with fewer sociodemographic resources reported later milestone attainment. Developmental science theories highlight how family/systems can directly influence child development and this study provides a clear link between family resources and child developmental progress. Clinical implications for this work could include supporting the larger family system to improve child milestone achievement.

Dataset Information

Self-assessment of functional ability in schizophrenia: milestone achievement and its relationship to accuracy of self-evaluation.

Publications

Self-assessment of functional ability in schizophrenia: milestone achievement and its relationship to accuracy of self-evaluation.

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