Children with diabetes mellitus: perception of their behavioural problems by parents and teachers

Court, S. D. M.; Sein, Eleanor; McCowen, C; Hackett, A. F.; Parkin, J M

doi:10.1016/0378-3782(88)90105-3

Cited by 12 publications

(11 citation statements)

References 9 publications

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“…This indicates that although mothers of children with adjustment problems and mothers of well-adjusted children were equally knowledgeable about diabetes and the management of its treatment protocol, mothers of children with adjustment problems were more anxious about their child's disease. Indeed, consistent with Court et al's (1988) ®nding, mothers of children with psychological problems were more likely to report that they had`nerve' problems.…”

Section: Demographic and Medical Pro Les And Maternal Diabetic Knowledgesupporting

confidence: 55%

“…It has also been used in many studies with young diabetic samples (e.g. Court, Sein, McCowen, Hackett, & Parkin, 1988;Fonagy, Moran, Lindsay, Kurtz, & Brown, 1987). The scale has a cut-off point of 13 and a total score ranging from 0 to 62.…”

Section: Psychological Functioningmentioning

confidence: 99%

“…Holden, Chmielewski, Nelson, Kager, & Foltz, 1997;Rovet et al, 1987), whereas others have not (e.g. Castro et al, 2000;Court et al, 1988). Indeed, in light of the failure of demographic and medical variables to discriminate reliably between well-adjusted children and children with poorer adjustment, Mullins et al (1995) comment that the`identi®cation of more meaningful variables becomes especially important from an applied, clinical intervention standpoint' (p. 174).…”

Section: Demographic and Medical Pro Les And Maternal Diabetic Knowledgementioning

confidence: 99%

“…Indeed, in light of the failure of demographic and medical variables to discriminate reliably between well-adjusted children and children with poorer adjustment, Mullins et al (1995) comment that the`identi®cation of more meaningful variables becomes especially important from an applied, clinical intervention standpoint' (p. 174). Similarly, Court et al (1988) conclude that`children with problems need to be identi®ed by some other means' (p. 252), and Castro et al (2000) note that the psychological adjustment of diabetic children should be considered within the context of disease-related demands. Findings here support these observations.…”

Section: Demographic and Medical Pro Les And Maternal Diabetic Knowledgementioning

confidence: 99%

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The adjustment to diabetes of school‐age children with psychological adjustment problems

Chisholm

2003

British J Health Psychol

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Section: Demographic and Medical Pro Les And Maternal Diabetic Knowledgesupporting

confidence: 55%

Section: Psychological Functioningmentioning

confidence: 99%

Section: Demographic and Medical Pro Les And Maternal Diabetic Knowledgementioning

confidence: 99%

Section: Demographic and Medical Pro Les And Maternal Diabetic Knowledgementioning

confidence: 99%

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The adjustment to diabetes of school‐age children with psychological adjustment problems

Chisholm

2003

British J Health Psychol

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“…"14 . The above disturbances as well as unfavorable family relationships have a Key Words: childhood diabetes mellitus, parents of diabetic children, multidisciplinary team, psychosocial problems, mortalityVOLUME 15, NO.…”

mentioning

confidence: 99%

The Multidisciplinary Team for Children and Adolescents with Diabetes Mellitus

Laron¹

2002

Journal of Pediatric Endocrinology and Metabolism

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Comparison of machine learning techniques for precision in measurement of glucose level in artificial pancreas

Yadav

Nilam

2023

Math Methods in App Sciences

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Precision in the measurement of glucose levels in the artificial pancreas is a challenging task and a mandatory requirement for the proper functioning of an artificial pancreas. A suitable machine learning (ML) technique for the measurement of glucose levels in an artificial pancreas may play a crucial role in the management of diabetes. Therefore in the present work, a comparison has been made among a few ML techniques for the measurement of glucose levels in the artificial pancreas because ML is an astounding technology of artificial intelligence and widely applicable in various fields such as medical science, robotics, and environmental science. The models, namely, decision tree (DT), random forest (RF), support vector machine (SVM), and K‐nearest neighbor (KNN), based on supervised learning, are proposed for the dataset of Pima Indian to predict and classify diabetes mellitus. Ensuring the predictions and accuracy up to the level of diabetes mellitus type 2 (DMT2), the comparative behavior of all four models has been discussed. The ML models developed here stratify and predict whether an individual is diabetic or not based on the features available in the dataset. The dataset passes through pre‐processing, and ML algorithms are fitted to train the dataset, and then the performance of the test results is discussed. An error matrix (EM) has been generated to measure the accuracy score of the models. The accuracies in the prediction and classification of DMT2 models are 71%, 77%, 78%, and 80% for DT, SVM, RF, and KNN algorithms, respectively. The KNN model has shown a more precise result in comparison to other models. The proposed methods have shown astounding behavior in terms of accuracy in the prediction of diabetes mellitus as compared to previously developed methods.

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Children with diabetes mellitus: perception of their behavioural problems by parents and teachers

Cited by 12 publications

References 9 publications

The adjustment to diabetes of school‐age children with psychological adjustment problems

The adjustment to diabetes of school‐age children with psychological adjustment problems

The Multidisciplinary Team for Children and Adolescents with Diabetes Mellitus

Comparison of machine learning techniques for precision in measurement of glucose level in artificial pancreas

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