Help seeking behavior by women experiencing intimate partner violence in india: A machine learning approach to identifying risk factors

• Published in: PloS one Vol. 17; no. 2; p. e0262538
• Main Authors: Dehingia, Nabamallika, Dey, Arnab K., McDougal, Lotus, McAuley, Julian, Singh, Abhishek, Raj, Anita
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 03.02.2022; Public Library of Science (PLoS)
• Subjects: Adolescent; Adult; Aggression; Alcohol use; Analysis; Biology and Life Sciences; Computer and Information Sciences; Coronaviruses; COVID-19; Datasets; Demographics; Domestic violence; Drinking behavior; Economics; Employment; Family violence; Female; Gender equity; Graduate studies; Health aspects; Health services; Health Surveys; Help seeking behavior; Humans; Husbands; Hypotheses; Independent variables; India - epidemiology; Injuries; Intimate partner violence; Intimate Partner Violence - psychology; Intimate Partner Violence - statistics & numerical data; Laws, regulations and rules; Learning algorithms; Machine Learning; Maternal & child health; Medical diagnosis; Medical research; Medicine; Medicine and Health Sciences; Methods; Middle Aged; Modelling; Pandemics; People and Places; Police; Prevention; Public health; Regression analysis; Regression models; Reproductive health; Risk analysis; Risk Factors; Social Sciences; Social services; Support services; Tests; Variables; Violence; Women; Womens health; Young Adult; India; United States > US; California
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0262538

Despite the low prevalence of help-seeking behavior among victims of intimate partner violence (IPV) in India, quantitative evidence on risk factors, is limited. We use a previously validated exploratory approach, to examine correlates of help-seeking from anyone (e.g. family, friends, police, doctor etc.), as well as help-seeking from any formal sources. We used data from a nationally-representative health survey conducted in 2015-16 in India, and included all variables in the dataset (~6000 variables) as independent variables. Two machine learning (ML) models were used- L-1, and L-2 regularized logistic regression models. The results from these models were qualitatively coded by researchers to identify broad themes associated with help-seeking behavior. This process of implementing ML models followed by qualitative coding was repeated until pre-specified criteria were met. Identified themes associated with help-seeking behavior included experience of injury from violence, husband's controlling behavior, husband's consumption of alcohol, and being currently separated from husband. Themes related to women's access to social and economic resources, such as women's employment, and receipt of maternal and reproductive health services were also noted to be related factors. We observed similarity in correlates for seeking help from anyone, vs from formal sources, with a greater focus on women being separated for help-seeking from formal sources. Findings highlight the need for community programs to reach out to women trapped in abusive relationships, as well as the importance of women's social and economic connectedness; future work should consider holistic interventions that integrate IPV screening and support services with women's health related services.