Journal of evaluation in clinical practice
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The birthweight of a newborn is critical to their health, development, and well-being. Previous studies that used maternal characteristics to predict birthweight did not employ a harmonised scale to assess the risk of low birthweight (LBW). ⋯ The scale, termed birthweight questionnaire, is a valuable tool for collecting data that could assist in assessing the risk of an LBW at every stage of pregnancy.
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Healthcare systems remain disease oriented despite growing sustainability concerns caused by inadequate management of patients with multimorbidity. Comprehensive care programmes (CCPs) can play an important role in streamlining care delivery, but large differences in setup and results hinder firm conclusions on their effectiveness. Many elements for successful implementation of CCPs are identified, but strategies to overcome barriers and embed programmes within health systems remain unknown. ⋯ The introduction of a CCP is feasible, and exploratory analysis on effectiveness shows lower hospital care use without decreasing patient satisfaction. However, this is accompanied by several challenges that show current fragmented systems still do not support implementation of integrated care initiatives. Overcoming those comes with substantial costs and may require a strong bottom-up implementation within a motivated team and actions on all levels of healthcare systems.
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The composition and amount of breast milk is affected by factors such as the duration and frequency of breastfeeding, the time between two breastfeeding sessions, the effectiveness of breastfeeding, breastfeeding technique, genetic characteristics of the mother and diet. Breast milk macronutrients are provided by milk synthesized in lactocytes, mother's diet and maternal stores. ⋯ As the weight of mothers increases, breast milk protein and carbohydrate levels increase. As breast milk macronutrients increase, babies' weight and height increase.
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Clinical abbreviations pose a challenge for clinical decision support systems due to their ambiguity. Additionally, clinical datasets often suffer from class imbalance, hindering the classification of such data. This imbalance leads to classifiers with low accuracy and high error rates. Traditional feature-engineered models struggle with this task, and class imbalance is a known factor that reduces the performance of neural network techniques. ⋯ Deep neural network methods, particularly Bi-LSTM, offer promising alternatives to traditional feature-engineered models for clinical abbreviation disambiguation. By employing data generation techniques, we can address the challenges posed by limited-resource and imbalanced clinical datasets. This approach leads to a significant improvement in model accuracy for clinical abbreviation disambiguation tasks.
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This paper explores how frontline nurses experienced the onset of the coronavirus disease (COVID-19) pandemic to provide appropriate care during a global health crisis. ⋯ Understanding the challenges faced by frontline nurses during the onset of the COVID-19 pandemic may help healthcare practitioners and policy makers to implement targeted interventions, support mechanisms and resource allocation strategies that enhance the well-being of frontline nurses and optimise patient care delivery during health crises.