A Digital Health Approach to Addressing Technology-Facilitated Gender-Based Violence and Mental Health Support in India
1
Programmes, Girl Effect, Gurugram, India
Popul. Med. 2026;8(Supplement Supplement 1):A938
ABSTRACT
INTRODUCTION:
India’s rapid digital expansion has intensified technology-facilitated gender-based violence (TFGBV), disproportionately affecting adolescent girls and young women. TFGBV is linked to adverse mental health outcomes, including anxiety, stress, social withdrawal, and reduced digital participation. Despite cybercrimes against women accounting for nearly 30% of all reported cybercrimes, awareness of online safety, reporting mechanisms, and support services remains limited. Only one in ten girls knows how to report online abuse, highlighting critical gaps in access to mental health–linked support and digital health information.
METHODS:
Girl Effect India (GEI) implemented a population-level digital health support ecosystem combining a unified Interactive Voice Response (IVR) helpline and an AI-enabled WhatsApp chatbot. The IVR provides a confidential, stigma-free entry point with guided information on online safety, digital rights, and support options, routing callers to vetted partner organisations. Rapid, user-centred iteration introduced age-based routing to safeguard minors, an information layer to support decision-making, and call-blocking features to mitigate misuse. Complementing the IVR, Bol Behen, GEI’s AI-enabled WhatsApp chatbot for adolescent girls and young women (18–24), integrated TFGBV-specific chatflows to support harm recognition and referrals.
RESULTS:
The ecosystem demonstrated strong demand for confidential support. Digital outreach generated 15,873 engagements, resulting in 4,786 incoming IVR calls, of which 492 were successfully connected to partner services. Age-based routing and call-blocking reduced operational disruptions, while the chatbot enabled multilingual, anonymous access to TFGBV-related mental health information, with user testing indicating improved clarity and perceived safety.
CONCLUSIONS:
Combining IVR and AI-enabled chatbot platforms can lower barriers to mental health–linked support for adolescent girls and young women experiencing TFGBV. By integrating accurate health information, safeguarded referral pathways, the model supports earlier help-seeking and strengthens linkages between digital engagement and offline services. This scalable digital health approach offers a solution for addressing online harm and its mental health consequences.
India’s rapid digital expansion has intensified technology-facilitated gender-based violence (TFGBV), disproportionately affecting adolescent girls and young women. TFGBV is linked to adverse mental health outcomes, including anxiety, stress, social withdrawal, and reduced digital participation. Despite cybercrimes against women accounting for nearly 30% of all reported cybercrimes, awareness of online safety, reporting mechanisms, and support services remains limited. Only one in ten girls knows how to report online abuse, highlighting critical gaps in access to mental health–linked support and digital health information.
METHODS:
Girl Effect India (GEI) implemented a population-level digital health support ecosystem combining a unified Interactive Voice Response (IVR) helpline and an AI-enabled WhatsApp chatbot. The IVR provides a confidential, stigma-free entry point with guided information on online safety, digital rights, and support options, routing callers to vetted partner organisations. Rapid, user-centred iteration introduced age-based routing to safeguard minors, an information layer to support decision-making, and call-blocking features to mitigate misuse. Complementing the IVR, Bol Behen, GEI’s AI-enabled WhatsApp chatbot for adolescent girls and young women (18–24), integrated TFGBV-specific chatflows to support harm recognition and referrals.
RESULTS:
The ecosystem demonstrated strong demand for confidential support. Digital outreach generated 15,873 engagements, resulting in 4,786 incoming IVR calls, of which 492 were successfully connected to partner services. Age-based routing and call-blocking reduced operational disruptions, while the chatbot enabled multilingual, anonymous access to TFGBV-related mental health information, with user testing indicating improved clarity and perceived safety.
CONCLUSIONS:
Combining IVR and AI-enabled chatbot platforms can lower barriers to mental health–linked support for adolescent girls and young women experiencing TFGBV. By integrating accurate health information, safeguarded referral pathways, the model supports earlier help-seeking and strengthens linkages between digital engagement and offline services. This scalable digital health approach offers a solution for addressing online harm and its mental health consequences.
| eISSN: | 2654-1459 |
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