4 research outputs found

    Testing the tripartite model in young adolescents:Is hyperarousal specific for anxiety and not depress ion?

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    Background: To clarify the distinction between anxiety and depression, the tripartite model was introduced. According to this model, physiological hyperarousal (PH, i.e. autonomic hyperactivity) is specific for anxiety and not depression. Research on the relation between anxiety, depression and physiological measures representing arousal is lacking. Methods: Parent- and self-reported anxiety and depressive problems were assessed using the CBCL and RCADS. Heart rate (HR), heart rate variability in the low frequency (HRV LF) and respiratory sinus arrythmia (RSA) were used as, indices for autonomic arousal. Results: Parent-reported anxiety was associated with low RSA in supine posture. This association was also found for self-reported anxiety problems, but only in boys. These findings point towards high arousal in anxiety. Self-reported depressive problems were associated with low HRV LF in standing posture and high RSA in supine posture in boys, pointing towards low arousal in depression. However, self-reported depressive problems were also associated with high HR in standing posture and with low HRV LF in supine posture in girls, suggesting high arousal in depression. Limitations: Although HRV LF in standing posture is primarily sympathetically mediated, and HRV LF in supine posture is primarily vagally mediated, the association between HRV LF and sympathetic versus vagal function is not exclusive. Thus, HRV LF measures are merely approaches of high or low arousal. Conclusions: Some evidence was found for hyperarousal in anxiety, but also for hyperarousal in depression. Apparently, the idea of hyperarousal in anxiety arid not in depression is too simple to reflect the more complex reality. (c) 2007 Published by Elsevier B.V.

    Detection of unsafety in families with parental and/or child developmental problems at the start of family support

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    Background Risk assessment is crucial in preventing child maltreatment as it can identify high-risk cases in need of child protection intervention. Despite this importance, there have been no validated risk assessment instruments available in the Netherlands for assessing the risk of child maltreatment. Therefore, the predictive validity of the California Family Risk Assessment (CFRA) was examined in Dutch families who received family support. In addition, the added value of a number of experimental items was examined. Finally, it was examined whether the predictive value of the instrument could be improved by modifying the scoring procedure. Methods Dutch families who experienced parenting and/or child developmental problems and were referred by the Centres for Youth and Family for family support between July 2009 and March 2011 were included. This led to a sample of 491 families. The predictive validity of the CFRA and the added value of the experimental items were examined by calculating AUC values. A CHAID analysis was performed to examine whether the scoring procedure could be improved. Results About half of the individual CFRA items were not related to future reports of child maltreatment. The predictive validity of the CFRA in predicting future reports of child maltreatment was found to be modest (AUC = .693). The addition of some of the experimental items and the modification of the scoring procedure by including only items that were significantly associated with future maltreatment reports resulted in a ‘high’ predictive validity (AUC = .795). Conclusions This new set of items might be a valuable instrument that also saves time because only variables that uniquely contribute to the prediction of future reports of child maltreatment are included. Furthermore, items that are perceived as difficult to assess by professionals, such as parental mental health problems or parents’ history of abuse/neglect, could be omitted without compromising predictive validity. However, it is important to examine the psychometric properties of this new set of items in a new dataset
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