Improving the outcome of infants born at <30 weeks' gestation - a randomized controlled trial of preventative care at home

Background: Early developmental interventions to prevent the high rate of neurodevelopmental problems in very preterm children, including cognitive, motor and behavioral impairments, are urgently needed. These interventions should be multi-faceted and include modules for caregivers given their high rates of mental health problems

Postpartum psychiatric disorders

Pregnancy is a complex and vulnerable period that presents a number of challenges to women, including the development of postpartum psychiatric disorders (PPDs). These disorders can include postpartum depression and anxiety, which are relatively common, and the rare but more severe postpartum psychosis. In addition, other PPDs can include obsessive–compulsive disorder, post-traumatic stress disorder and eating disorders. The aetiology of PPDs is a complex interaction of psychological, social and biological factors, in addition to genetic and environmental factors. The goals of treating postpartum mental illness are reducing maternal symptoms and supporting maternal–child and family functioning. Women and their families should receive psychoeducation about the illness, including evidence-based discussions about the risks and benefits of each treatment option. Developing effective strategies in global settings that allow the delivery of targeted therapies to women with different clinical phenotypes and severities of PPDs is essential

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This paper uses the prime program decomposition of a program as the basis for a measure that closely correlates with our intuitive notion of program complexity. This measure is based upon the information theory ideas of randomness and entropy such that results about structured programming, data abstractions, and other programming paradigms can be stated in quantitative terms, and empirical means can be used to validate the assumptions used to develop the model. As a graph-based model, it can be applied to several graphical examples as extensions not otherwise available to source-code based models. This paper introduces the measure, derives several properties for it, and gives some simple examples to demonstrate that the measure is a plausible approximation of our notions concerning structured programming. 1. INTRODU~ION This paper develops a program complexity metric based upon the information theoretic concepts of complexity and randomness to quantify programming concepts like structured programming and data abstractions. The graph-based prime program decomposition of a flow graph is used to develop these ideas. W/y do we need such Q measure? Program complexity is of significant interest within software engineering. Numerous “truths ” are afforde