Other Social SciencesMixed Methods
Gender-Based Violence Prevention: From Bystander Programs to Data-Driven Policy
One in three women worldwide experiences physical or sexual violence. Prevention has shifted from solely supporting survivors to systemic approaches: bystander training, data-driven risk prediction, economic empowerment, and culturally tailored programs that challenge the norms enabling violence.
By Sean K.S. Shin
This blog summarizes research trends based on published paper abstracts. Specific numbers or findings may contain inaccuracies. For scholarly rigor, always consult the original papers cited in each post.
Gender-based violence (GBV) affects approximately 736 million women globally—one in three. It is the most widespread human rights violation in the world, cutting across every culture, economic class, and education level. While crisis response—shelters, hotlines, legal protection—remains essential, the field is shifting toward primary prevention: stopping violence before it occurs by transforming the social conditions that enable it.
Why It Matters
GBV imposes enormous human and economic costs. The World Bank estimates that intimate partner violence alone costs the global economy approximately 5% of GDP through lost productivity, healthcare expenditure, and criminal justice costs. Beyond economics, GBV perpetuates intergenerational cycles of trauma, limits women's educational and economic participation, and undermines development goals across every sector.
The Research Landscape
University Bystander Intervention
Barter and Farrelly (2024), with 2 citations, evaluate The Intervention Initiative (TII)—a UK university bystander program recommended by Universities UK as a key prevention strategy. Bystander programs train community members to recognize warning signs of violence and intervene safely. The feasibility study finds that while the program increases bystander confidence and knowledge, implementation challenges include low male participation, difficulty sustaining engagement beyond initial training, and the need for institutional culture change alongside individual skills.
Machine Learning for Prevention
Ekeh and Odionu (2025), with 3 citations, explore the frontier of data analytics and machine learning for GBV prevention and policy design. ML models can identify geographic hotspots, predict escalation patterns from service utilization data, and evaluate intervention effectiveness at population scale. Their framework integrates health records, police reports, social services data, and economic indicators to create predictive models that guide resource allocation. Critical ethical considerations include privacy, algorithmic bias, and the risk of surveillance targeting vulnerable populations.
Economic Empowerment Approach
Aruni and Safrida (2025), with 2 citations, analyze the intersection of women's economic empowerment and domestic violence prevention in North Aceh, Indonesia. Using a Gender Transformative (GT) approach, they find that economic programs alone are insufficient—women with increased income but unchanged household power dynamics may face backlash violence. Effective prevention requires combining economic empowerment with community-level gender norm change and male engagement.
Culturally Tailored Prevention in Ghana
Seidu and Compton (2025) pilot a primary prevention program among health sciences students in Ghana. The program adapts evidence-based curricula to Ghanaian cultural context, addressing specific norms around masculinity, sexual entitlement, and institutional silence. Pilot results show significant improvements in knowledge, attitudes, and bystander behavior, demonstrating that prevention programs must be culturally grounded to be effective.
Prevention Approaches
<
| Level | Strategy | Example | Evidence Base |
|---|
| Individual | Skills training | Self-defense, safety planning | Moderate |
| Relationship | Couples counseling, parenting | Healthy relationship programs | Growing |
| Community | Bystander training, norm change | The Intervention Initiative | Strong |
| Societal | Laws, economic policy, media | Economic empowerment, education | Strong (long-term) |
| Data-driven | ML prediction, hotspot mapping | Resource allocation optimization | Emerging |
What To Watch
Technology-facilitated abuse—stalkerware, image-based abuse, AI-generated deepfake pornography—is creating new forms of GBV that existing legal frameworks struggle to address. Prevention programs are expanding to address online spaces alongside physical ones. The integration of GBV prevention into climate adaptation planning recognizes that climate-driven displacement and resource scarcity intensify violence against women and girls.
Gender-based violence (GBV) affects approximately 736 million women globally—one in three. It is the most widespread human rights violation in the world, cutting across every culture, economic class, and education level. While crisis response—shelters, hotlines, legal protection—remains essential, the field is shifting toward primary prevention: stopping violence before it occurs by transforming the social conditions that enable it.
Why It Matters
GBV imposes enormous human and economic costs. The World Bank estimates that intimate partner violence alone costs the global economy approximately 5% of GDP through lost productivity, healthcare expenditure, and criminal justice costs. Beyond economics, GBV perpetuates intergenerational cycles of trauma, limits women's educational and economic participation, and undermines development goals across every sector.
The Research Landscape
University Bystander Intervention
Barter and Farrelly (2024), with 2 citations, evaluate The Intervention Initiative (TII)—a UK university bystander program recommended by Universities UK as a key prevention strategy. Bystander programs train community members to recognize warning signs of violence and intervene safely. The feasibility study finds that while the program increases bystander confidence and knowledge, implementation challenges include low male participation, difficulty sustaining engagement beyond initial training, and the need for institutional culture change alongside individual skills.
Machine Learning for Prevention
Ekeh and Odionu (2025), with 3 citations, explore the frontier of data analytics and machine learning for GBV prevention and policy design. ML models can identify geographic hotspots, predict escalation patterns from service utilization data, and evaluate intervention effectiveness at population scale. Their framework integrates health records, police reports, social services data, and economic indicators to create predictive models that guide resource allocation. Critical ethical considerations include privacy, algorithmic bias, and the risk of surveillance targeting vulnerable populations.
Economic Empowerment Approach
Aruni and Safrida (2025), with 2 citations, analyze the intersection of women's economic empowerment and domestic violence prevention in North Aceh, Indonesia. Using a Gender Transformative (GT) approach, they find that economic programs alone are insufficient—women with increased income but unchanged household power dynamics may face backlash violence. Effective prevention requires combining economic empowerment with community-level gender norm change and male engagement.
Culturally Tailored Prevention in Ghana
Seidu and Compton (2025) pilot a primary prevention program among health sciences students in Ghana. The program adapts evidence-based curricula to Ghanaian cultural context, addressing specific norms around masculinity, sexual entitlement, and institutional silence. Pilot results show significant improvements in knowledge, attitudes, and bystander behavior, demonstrating that prevention programs must be culturally grounded to be effective.
Prevention Approaches
<
| Level | Strategy | Example | Evidence Base |
|---|
| Individual | Skills training | Self-defense, safety planning | Moderate |
| Relationship | Couples counseling, parenting | Healthy relationship programs | Growing |
| Community | Bystander training, norm change | The Intervention Initiative | Strong |
| Societal | Laws, economic policy, media | Economic empowerment, education | Strong (long-term) |
| Data-driven | ML prediction, hotspot mapping | Resource allocation optimization | Emerging |
What To Watch
Technology-facilitated abuse—stalkerware, image-based abuse, AI-generated deepfake pornography—is creating new forms of GBV that existing legal frameworks struggle to address. Prevention programs are expanding to address online spaces alongside physical ones. The integration of GBV prevention into climate adaptation planning recognizes that climate-driven displacement and resource scarcity intensify violence against women and girls.
References (8)
[1] Barter, C.A., Bracewell, K., & Farrelly, N. (2024). University Bystander Intervention Programme. Journal of Gender-Based Violence.
[2] Ekeh, A.H., Apeh, C.E., & Odionu, C.S. (2025). Data Analytics and ML for GBV Prevention. Global Journal of Applied Business Research.
[3] Aruni, F., Safrida, S., & Safrida, N. (2025). Economic Empowerment and DV Prevention in Aceh. JBA.
[4] Seidu, A., Dickson, K., & Compton, S.D. (2025). SGBV Prevention in Ghana. Women's Health.
Barter, C., Bracewell, K., Farrelly, N., Clelland, A. K., & Chantler, K. (2025). Prevention of sexual violence and domestic abuse through a university bystander intervention programme: learning from a UK feasibility study. Journal of Gender-Based Violence, 9(1), 22-39.
Amazing Hope Ekeh, Charles Elachi Apeh, Chinekwu Somtochukwu Odionu, & Blessing Austin-Gabriel (2025). Data analytics and machine learning for gender-based violence prevention: A framework for policy design and intervention strategies. Gulf Journal of Advance Business Research, 3(2), 323-347.
Aruni, F., Safrida, S., Safrida, N., Bahri, S., & Kurniawan, R. (2025). Analysis of Women’s Economic Empowerment and Domestic Violence Prevention Policy Implementation based on Gender Transformative (GT) Approach in North Aceh Regency. Jurnal Borneo Administrator, 21(1), 91-104.
Seidu, A., Dickson, K. S., Compton, S. D., Owusu-Antwi, R., Robba, M. J. B., Valadez, C. A., et al. (2025). Expanding a primary prevention program to address sexual and gender-based violence among health sciences students in Ghana: A pilot study. Women's Health, 21.