Domestic Violence in Atlanta, Georgia Before and During COVID-19

Evans, Dabney P.; Hawk, Shila René; Ripkey, Carrie E

doi:10.1089/vio.2020.0061

Cited by 65 publications

(81 citation statements)

References 12 publications

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“…Piquero et al (2020) study crime data from 2020 alone and are therefore unable to account for important seasonal variation (e.g., Fig.1in this paper) Evans et al (2021). studies aggravated domestic assaults in Atlanta and finds larger percent growth between 2019 and 2020 in the first weeks of the year than during the shutdown period (Fig.3).…”

mentioning

confidence: 85%

Effects of COVID-19 Shutdowns on Domestic Violence in US Cities

Miller¹,

Segal²,

Spencer³

2021

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mentioning

confidence: 85%

Effects of COVID-19 Shutdowns on Domestic Violence in US Cities

Miller¹,

Segal²,

Spencer³

2021

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“…Besides the academic drawbacks, school closure and lockdown have led to an increase of child abuse and gender violence (Evans et al, 2020;Lee et al, 2021;Masilamani et al, 2021). Unfortunately, individuals with intellectual disability are especially vulnerable in this vein (Courtenay and Perera, 2020), and girls and women with disabilities are at a higher risk of suffering from gender violence (Samaila et al, 2020).…”

Section: Impact Of School Closure On Students With Special Educationamentioning

confidence: 99%

Beyond the School Walls: Keeping Interactive Learning Environments Alive in Confinement for Students in Special Education

et al. 2021

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The COVID-19 pandemic and the accompanying safety measures, including confinement, has meant an unprecedented challenge for the world population today. However, it has entailed additional difficulties for specific populations, including children and people with disabilities. Being out of school for months has reduced the learning opportunities for many children, such as those with less academic resources at home or with poorer technological connectivity. For students with disabilities, it has entailed losing the quality of the special attention they often need, in addition to a more limited understanding of the situation. In this context, a case study was conducted in a special education classroom of a secondary education school. This class started implementing Dialogic Literary Gatherings with their special education students before the COVID-19 confinement and continued online during the confinement. Qualitative data was collected after a period of implementation of the gatherings showing positive impacts on the participants. The case study shows that interactive learning environments such as the Dialogic Literary Gatherings can provide quality distance learning for students with disabilities, contributing to overcome some of the barriers that the pandemic context creates for the education of these students.

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“…Various statistical techniques, such as frequencies, mean, standard deviation, chi-squared tests, odds ratio, analysis of variance (ANOVA), and correlation were used. Evans et al [14] examined the disparity in records of cases of domestic abuse from police statistics in Atlanta, Georgia, by compiling the residential felony counts mainstreamed to the metropolitan area. They analyzed the fluctuations and severity of the residents and crossed the rows with these studies.…”

Section: Related Workmentioning

confidence: 99%

Prediction on Domestic Violence in Bangladesh during the COVID-19 Outbreak Using Machine Learning Methods

Hossain

Asadullah

Rahaman

et al. 2021

ASI

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The COVID-19 outbreak resulted in preventative measures and restrictions for Bangladesh during the summer of 2020—these unstable and stressful times led to multiple social problems (e.g., domestic violence and divorce). Globally, researchers, policymakers, governments, and civil societies have been concerned about the increase in domestic violence against women and children during the ongoing COVID-19 pandemic. In Bangladesh, domestic violence against women and children has increased during the COVID-19 pandemic. In this article, we investigated family violence among 511 families during the COVID-19 outbreak. Participants were given questionnaires to answer, for a period of over ten days; we predicted family violence using a machine learning-based model. To predict domestic violence from our data set, we applied random forest, logistic regression, and Naive Bayes machine learning algorithms to our model. We employed an oversampling strategy named the Synthetic Minority Oversampling Technique (SMOTE) and the chi-squared statistical test to, respectively, solve the imbalance problem and discover the feature importance of our data set. The performances of the machine learning algorithms were evaluated based on accuracy, precision, recall, and F-score criteria. Finally, the receiver operating characteristic (ROC) and confusion matrices were developed and analyzed for three algorithms. On average, our model, with the random forest, logistic regression, and Naive Bayes algorithms, predicted family violence with 77%, 69%, and 62% accuracy for our data set. The findings of this study indicate that domestic violence has increased and is highly related to two features: family income level during the COVID-19 pandemic and education level of the family members.

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Domestic Violence in Atlanta, Georgia Before and During COVID-19

Cited by 65 publications

References 12 publications

Effects of COVID-19 Shutdowns on Domestic Violence in US Cities

Effects of COVID-19 Shutdowns on Domestic Violence in US Cities

Beyond the School Walls: Keeping Interactive Learning Environments Alive in Confinement for Students in Special Education

Prediction on Domestic Violence in Bangladesh during the COVID-19 Outbreak Using Machine Learning Methods

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