Psychosocial Impact of 8 Weeks COVID-19 Quarantine on Italian Parents and their Children.

OBJECTIVES: Italy was affected greatly by Coronavirus disease 2019 (COVID-19), emerging mainly in the Italian province of Lombardy. This outbreak led to profound governmental interventions along with a strict quarantine. This quarantine may have psychosocial impact on children and parents in particular. The aim of this study was to evaluate the impact of 8 weeks COVID-19 quarantine on psychosocial functioning of Italian parents and their children. METHODS: In this cross-sectional survey, we included parents and children resided in Italy during the 8 weeks COVID-19 quarantine. We evaluated social and emotional functioning, clinical symptoms possibly related to emotional distress, and change in perspectives using a questionnaire. RESULTS: The majority of 2315 parents (98% mothers) frequently experienced fear of getting ill (92%) and fluctuating moods (84%), the latter showing correlation to experiencing stress due to being in continuous close vicinity to their children (77%, r = 0.33). Parents reported a positive effect on the relationship with their partner (79%) and their children (89%). Irritability in children was frequent (74%) and correlated to parental fluctuating moods (r = 0.40). The vast majority of the participants (91%) reported that their perspectives for the future had changed. CONCLUSIONS FOR PRACTICE: Our findings suggest a profound impact of the COVID-19 quarantine on emotional functioning of parents and their children in Italy. Despite the protective measure of quarantine against national viral spread and subsequent infection, health care professionals should be aware of this emotional impact, in order to develop protective or therapeutic interventions.

Improved Overlap-based Undersampling for Imbalanced Dataset Classification with Application to Epilepsy and Parkinson's Disease.

Classification of imbalanced datasets has attracted substantial research interest over the past decades. Imbalanced datasets are common in several domains such as health, finance, security and others. A wide range of solutions to handle imbalanced datasets focus mainly on the class distribution problem and aim at providing more balanced datasets by means of resampling. However, existing literature shows that class overlap has a higher negative impact on the learning process than class distribution. In this paper, we propose overlap-based undersampling methods for maximizing the visibility of the minority class instances in the overlapping region. This is achieved by the use of soft clustering and the elimination threshold that is adaptable to the overlap degree to identify and eliminate negative instances in the overlapping region. For more accurate clustering and detection of overlapped negative instances, the presence of the minority class at the borderline areas is emphasized by means of oversampling. Extensive experiments using simulated and real-world datasets covering a wide range of imbalance and overlap scenarios including extreme cases were carried out. Results show significant improvement in sensitivity and competitive performance with well-established and state-of-the-art methods.

Response to Discussion on "Improved Overlap-Based Undersampling for Imbalanced Dataset Classification with Application to Epilepsy and Parkinson's Disease,".

In the paper Improved Overlap-Based Undersampling for Imbalanced Dataset Classification with Application to Epilepsy and Parkinson's Disease, the authors introduced two new methods that address the class overlap problem in imbalanced datasets. The methods involve identification and removal of potentially overlapped majority class instances. Extensive evaluations were carried out using 136 datasets and compared against several state-of-the-art methods. Results showed competitive performance with those methods, and statistical tests proved significant improvement in classification results. The discussion on the paper related to the behavioral analysis of class overlap and method validation was raised by Fernández. In this article, the response to the discussion is delivered. Detailed clarification and supporting evidence to answer all the points raised are provided.