Predicting the Vulnerability of Women to Intimate Partner Violence in South Africa: Evidence from Tree-based Machine Learning Techniques

Predicting Intimate Partner Violence Perpetration Among Young Adults Experiencing Homelessness in Seven U.S. Cities Using Interpretable Machine Learning

Young adults experiencing homelessness (YAEH) are at higher risk for intimate partner violence (IPV) victimization than their housed peers. This is often due to their increased vulnerability to abuse and victimization before and during homelessness, which can result in a cycle of violence in which YAEH also perpetrates IPV. Identifying and addressing factors contributing to IPV perpetration at an early stage can reduce the risk of IPV. Yet to date, research examining YAEH's IPV perpetration is scarce and has largely employed conventional statistical approaches that are limited in modeling this complex phenomenon. To address these gaps, this study used an interpretable machine learning approach to answer the research question: What are the most salient predictors of IPV perpetration among a large sample of YAEH in seven U.S. cities? Participants (N = 1,426) on average were 21 years old (SD = 2.09) and were largely cisgender males (59%) and racially/ethnically diverse (81% were from historically excluded racial/ethnic groups; i.e., African American, Latino/a, American Indian, Asian or Pacific Islander, and mixed race/ethnicity). Over one-quarter (26%) reported IPV victimization, and 20% reported IPV perpetration while homeless. Experiencing IPV victimization while homeless was the most important factor in predicting IPV perpetration. An additional 11 predictors (e.g., faced frequent discrimination) were positively associated with IPV perpetration, whereas 8 predictors (e.g., reported higher scores of mindfulness) were negatively associated. These findings underscore the importance of developing and implementing effective interventions with YAEH that can prevent IPV, particularly those that recognize the positive association between victimization and perpetration experiences.

Harnessing the Power of Machine Learning to Prevent Gender-Based Violence: Using Big Data Techniques to Enhance Research on Violence Against Women

Data on incidents involving violence against women is becoming increasingly accessible, thanks in part to the Violence Against Women Act of 1994 and its reauthorizations. Technology facilitates the sharing of data and qualitative experiences in online forums and social media platforms, which has led to growing demand for analytical tools like data mining and machine learning algorithms to handle these large-scale data sources. This research note provides an overview of the application of big data techniques to research on violence against women and contributes to the discussion on ethical concerns for artificial intelligence in the violence against women research field.

Violence Against Women and Girls in Zimbabwe: A Review of a Decade of the Empirical Literature

Violence against women and girls (VAWG) is a complex social problem affecting many women globally. In Zimbabwe, intimate partner violence (IPV) and child marriages remain persistent public health problems with detrimental effects on the health and well-being of women and girls. Statistics show that Zimbabwe has one of the highest rates of IPV and child marriage in sub-Saharan Africa. Given this background, this paper systematically reviewed published research on VAWG in Zimbabwe from 2012 to 2022 to identify research gaps. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, we systematically searched for literature across five electronic databases: Web of Science, Medline, Psych-Info via EBSCO-Host, and Google Scholar. Our initial search yielded 261 articles, of which only 45 met our inclusion criteria. We summarized these studies using thematic analysis and performed a quality assessment using the Mixed Methods Appraisal Tool. The findings revealed several gaps, including a limited focus on relational and perpetrator perspectives, insufficient attention to other forms of VAWG such as non-partner sexual violence, rape, and trafficking, and a lack of studies on marginalized groups such as people with disabilities, sex workers, and same-sex couples. In addition, there were no longitudinal studies examining trends and dynamics of VAWG over extended periods or comparing different geographical regions. Few studies also focused on the evaluation of interventions. Although significant progress has been made in addressing VAWG, this review underscores the need for more research to fill these gaps for effective and evidence-based policymaking and response strategies.

A Systematic Review and Meta-Analysis of the Association between Childhood Exposure to Intimate Partner Violence and Intimate Partner Violence Victimization/Perpetration in Adulthood in Africa

Intimate partner violence (IPV) is widespread in many African countries. Evidence, mainly from Western countries, shows that exposure to IPV in childhood is an important risk factor for experiences of IPV in adulthood. However, to date, no systematic review has synthesized the evidence on this association for individuals living in Africa, which is the goal of the current study. We used three search strategies: database searches (e.g., MEDLINE and PsycINFO), manual searches, and machine learning tools (e.g., Connected Papers). We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and included peer-reviewed studies (in French or English) that reported quantitative or qualitative associations between childhood exposure to IPV and later IPV victimization/perpetration. A total of 48 studies from 29 African countries were included (N = 520,000 participants). Pooled effects indicated an association between childhood exposure to IPV and IPV victimization for females (odds ratio [OR] = 2.46, 95% CI [2.09, 2.91], p < .001) and males (OR = 1.76, 95% CI [1.57, 1.97], p < .001). Similarly, males (OR = 1.92, 95% CI [1.60, 2.29], p < .001) and females (OR = 3.04, 95% CI [2.51, 3.69], p < .001) who were exposed to IPV in childhood were more likely to perpetrate IPV compared to those with no childhood exposure. Effect sizes varied substantially across studies (0.89-5.66), suggesting that other risk factors should be considered in future studies. This review provides unique insights on cycles of IPV in Africa that may usefully inform practice and research.

Using Machine Learning Prediction to Create a 15-question IPV Measurement Tool

Domestic violence, especially intimate partner violence (IPV), is an important issue worldwide, especially in India. Those that experience it may not always be able to come forward or have access to the required social support to act against it. We use National Family Health Survey data (n = 66,013 women) to create machine learning models which can predict IPV instances with a recall of 78%. We use the top 15 best predicting questions that avoid sensitive issues to create a field tool that frontline health workers can use to identify women with a high risk of IPV and provide the support they need.

A comparative study of machine learning algorithms for predicting domestic violence vulnerability in Liberian women

Domestic violence against women is a prevalent in Liberia, with nearly half of women reporting physical violence. However, research on the biosocial factors contributing to this issue remains limited. This study aims to predict women's vulnerability to domestic violence using a machine learning approach, leveraging data from the Liberian Demographic and Health Survey (LDHS) conducted in 2019-2020. We employed seven machine learning algorithms to achieve this goal, including ANN, KNN, RF, DT, XGBoost, LightGBM, and CatBoost. Our analysis revealed that the LightGBM and RF models achieved the highest accuracy in predicting women's vulnerability to domestic violence in Liberia, with 81% and 82% accuracy rates, respectively. One of the key features identified across multiple algorithms was the number of people who had experienced emotional violence. These findings offer important insights into the underlying characteristics and risk factors associated with domestic violence against women in Liberia. By utilizing machine learning techniques, we can better predict and understand this complex issue, ultimately contributing to the development of more effective prevention and intervention strategies.

Predicting the HIV/AIDS Knowledge among the Adolescent and Young Adult Population in Peru: Application of Quasi-Binomial Logistic Regression and Machine Learning Algorithms

Inadequate knowledge is one of the principal obstacles for preventing HIV/AIDS spread. Worldwide, it is reported that adolescents and young people have a higher vulnerability of being infected. Thus, the need to understand youths' knowledge towards HIV/AIDS becomes crucial. This study aimed to identify the determinants and develop a predictive model to estimate HIV/AIDS knowledge among this target population in Peru. Data from the 2019 DHS Survey were used. The software RStudio and RapidMiner were used for quasi-binomial logistic regression and computational model building, respectively. Five classification algorithms were considered for model development and their performance was assessed using accuracy, sensitivity, specificity, FPR, FNR, Cohen's kappa, F1 score and AUC. The results revealed an association between 14 socio-demographic, economic and health factors and HIV/AIDS knowledge. The accuracy levels were estimated between 59.47 and 64.30%, with the random forest model showing the best performance (64.30%). Additionally, the best classifier showed that the gender of the respondent, area of residence, wealth index, region of residence, interviewee's age, highest educational level, ethnic self-perception, having heard about HIV/AIDS in the past, the performance of an HIV/AIDS screening test and mass media access have a major influence on HIV/AIDS knowledge prediction. The results suggest the usefulness of the associations found and the random forest model as a predictor of knowledge of HIV/AIDS and may aid policy makers to guide and reinforce the planning and implementation of healthcare strategies.

Harnessing Machine Learning in Tackling Domestic Violence-An Integrative Review

Domestic violence (DV) is a public health crisis that threatens both the mental and physical health of people. With the unprecedented surge in data available on the internet and electronic health record systems, leveraging machine learning (ML) to detect obscure changes and predict the likelihood of DV from digital text data is a promising area health science research. However, there is a paucity of research discussing and reviewing ML applications in DV research.

METHODS

We extracted 3588 articles from four databases. Twenty-two articles met the inclusion criteria.

RESULTS

Twelve articles used the supervised ML method, seven articles used the unsupervised ML method, and three articles applied both. Most studies were published in Australia (n = 6) and the United States (n = 4). Data sources included social media, professional notes, national databases, surveys, and newspapers. Random forest (n = 9), support vector machine (n = 8), and naïve Bayes (n = 7) were the top three algorithms, while the most used automatic algorithm for unsupervised ML in DV research was latent Dirichlet allocation (LDA) for topic modeling (n = 2). Eight types of outcomes were identified, while three purposes of ML and challenges were delineated and are discussed.

CONCLUSIONS

Leveraging the ML method to tackle DV holds unprecedented potential, especially in classification, prediction, and exploration tasks, and particularly when using social media data. However, adoption challenges, data source issues, and lengthy data preparation times are the main bottlenecks in this context. To overcome those challenges, early ML algorithms have been developed and evaluated on DV clinical data.

A Novel Technique to Identify Intimate Partner Violence in a Hospital Setting

Introduction

Intimate partner violence (IPV) is defined as sexual, physical, psychological, or economic violence that occurs between current or former intimate partners. Victims of IPV may seek care for violence-related injuries in healthcare settings, which makes recognition and intervention in these facilities critical. In this study our goal was to develop an algorithm using natural language processing (NLP) to identify cases of IPV within emergency department (ED) settings.

Methods

In this observational cohort study, we extracted unstructured physician and advanced practice provider, nursing, and social worker notes from hospital electronic health records (EHR). The recorded clinical notes and patient narratives were screened for a set of 23 situational terms, derived from the literature on IPV (ie, assault by spouse), along with an additional set of 49 extended situational terms, extracted from known IPV cases (ie, attack by spouse). We compared the effectiveness of the proposed model with detection of IPV-related International Classification of Diseases, 10th Revision, codes.

Results

We included in the analysis a total of 1,064,735 patient encounters (405,303 patients who visited the ED of a Level I trauma center) from January 2012–August 2020. The outcome was identification of an IPV-related encounter. In this study we used information embedded in unstructured EHR data to develop a NLP algorithm that employs clinical notes to identify IPV visits to the ED. Using a set of 23 situational terms along with 49 extended situational terms, the algorithm successfully identified 7,399 IPV-related encounters representing 5,975 patients; the algorithm achieved 99.5% precision in detecting positive cases in our sample of 1,064,735 ED encounters.

Conclusion

Using a set of pre-defined IPV-related terms, we successfully developed a novel natural language processing algorithm capable of identifying intimate partner violence.

Prevalence and determinants of wife-beating in Bangladesh: evidence from a nationwide survey

BACKGROUND

Intimate partner violence (IPV) is a global public health concern, with women in low- and middle-income countries (LMICs) bearing a disproportionately high burden. This study investigates the prevalence and factors correlated with attitudes regarding wife-beating among Bangladeshi women in urban-rural contexts.

METHODS

A sample of 13,033 urban women and 51,344 rural women data from the Bangladesh Multiple Indicator Cluster Survey (MICS) 2019 were analyzed using the Chi-square test and ordinal logistic regression model.

RESULTS

The findings reveal that arguing with her husband is the widespread reason for wife-beating in Bangladesh (urban: 17.3%, rural: 21.9%), followed by neglecting the children (urban: 12.7%, rural: 15.8%). About 8% of urban women and 10% of rural women favoured the opinion that refusing to involve sexual intercourse is a legitimate justification for wife-beating. In comparison, around 5% feel that a husband has a right to beat his wife due to burning food. The respondents' age, education, marital status, number of children, socioeconomic level, any health or physical difficulty, having problems becoming pregnant, and the husband's age are all significant factors in justifying wife-beating.

CONCLUSIONS

Bangladesh has a massive challenge in eliminating IPV. Women from lower socioeconomic classes, low levels of education, other challenges, and residents of rural areas are particularly more vulnerable than their urban counterparts. Therefore, it is vital to develop a proper action plan that considers women's education and occupation to raise awareness of the various implications of wife-beating in women, particularly in Bangladesh's rural areas.

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

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.