Gender-Based Violence in the Asia-Pacific Region during COVID-19: A Hidden Pandemic behind Closed Doors.
Michiko HayashiAnna Durrance-BagaleManar MarzoukMengieng UngSze Tung LamPearlyn NeoNatasha HowardPublished in: International journal of environmental research and public health (2022)
Since the early stages of the COVID-19 pandemic, there have been reports of increased violence against women globally. We aimed to explore factors associated with reported increases in gender-based violence (GBV) during the pandemic in the Asia-Pacific region. We conducted 47 semi-structured interviews with experts working in sexual and reproductive health in 12 countries in the region. We analysed data thematically, using the socio-ecological framework of violence. Risks associated with increased GBV included economic strain, alcohol use and school closures, together with reduced access to health and social services. We highlight the need to address heightened risk factors, the importance of proactively identifying instances of GBV and protecting women and girls through establishing open and innovative communication channels, along with addressing underlying issues of gender inequality and social norms. Violence is exacerbated during public health crises, such as the COVID-19 pandemic. Identifying and supporting women at risk, as well as preventing domestic violence during lockdowns and movement restrictions is an emerging challenge. Our findings can help inform the adoption of improved surveillance and research, as well as innovative interventions to prevent violence and detect and protect victims.
Keyphrases
- mental health
- public health
- polycystic ovary syndrome
- risk factors
- sars cov
- healthcare
- intimate partner violence
- coronavirus disease
- physical activity
- insulin resistance
- pregnancy outcomes
- metabolic syndrome
- electronic health record
- risk assessment
- breast cancer risk
- emergency department
- cervical cancer screening
- machine learning
- social media
- artificial intelligence
- data analysis
- deep learning