Machine learning analysis of non-marital sexual violence in India

Adolescent Sexual Behavior in Rural Central India: Challenges and Interventions

Adolescence is a crucial life stage marked by significant physical, psychological, and societal changes. With India projected to have the highest population of teenagers by 2025, understanding adolescent sexual behavior in rural central India is essential due to its unique social and cultural contexts. This article reviews existing literature to explore the prevalence, risk factors, and consequences of teenage sexual behavior in rural central India. It highlights the challenges posed by societal taboos, limited access to sexual health information and services, and the impact of poverty on adolescents' sexual behavior and health outcomes. To address these issues, comprehensive sex education, improved access to contraception and reproductive health services, and efforts to overcome cultural and societal norms are crucial. The article discusses the initiatives undertaken by the government and non-governmental organizations (NGOs) to tackle adolescent sexual behavior and emphasizes the need for a multifaceted approach that addresses systemic issues while empowering adolescents. It concludes by suggesting future research directions and policy recommendations aimed at promoting safe sexual behavior among rural adolescents in central India. This article will discuss the complexity of adolescent sexual behavior in rural central India, its origins, and the challenges faced by medical decision-makers.

Using machine learning to understand determinants of IUD use in India: Analyses of the National Family Health Surveys (NFHS-4)

Intra-uterine devices (IUDs) are a safe and effective method to delay or space pregnancies and are available for free or at low cost in the Indian public health system; yet, IUD uptake in India remains low. Limited quantitative research using national data has explored factors that may affect IUD use. Machine Learning (ML) techniques allow us to explore determinants of low prevalence behaviors in survey research, such as IUD use. We applied ML to explore the determinants of IUD use in India among married women in the 4th National Family Health Survey (NFHS-4; N = 499,627), which collects data on demographic and health indicators among women of childbearing age. We conducted ML logistic regression (lasso and ridge) and neural network approaches to assess significant determinants and used iterative thematic analysis (ITA) to offer insight into related variable constructs generated from a series of regularized models. We found that couples’ shared family planning (FP) goals were the strongest determinants of IUD use, followed by receipt of FP services and desire for no more children, higher wealth and education, and receipt of maternal and child health services. Findings highlight the importance of male engagement and family planning services for IUD uptake and the need for more targeted efforts to support awareness of IUD as an option for spacing, especially for those of lower SES and with lower access to care.

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Prevalence of IUD use has remained extremely low in India for the past two decade.

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This paper used machine learning models with iterative thematic analysis to explore potential predictors of IUD use among currently married women in India.

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The strongest predictor of IUD use was shared marital family planning goals between the male and female partners.

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Prior access to Family Planning services and couples' desire for no more children were also found to be predictive of IUD use in India.

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Findings also highlight the importance of male engagement and family planning services for IUD uptake.

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

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSION

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.