Mental health and violence against women in Afghanistan, India and Sri Lanka: a situation analysis
Bibliographic record
Abstract
Background Globally, 10–53% of ever-partnered women have experienced physical or sexual intimate partner violence over their lifetime. Women survivors of violence are at high risk of poor mental health. In this study, we investigate women’s exposure to violence and mental health conditions in Afghanistan, India and Sri Lanka, while considering the policy and service contexts. Methods A situation analysis tool was developed for the study. We extracted information from grey and peer-reviewed literature and other publicly available data investigating the prevalence of violence against women and mental health conditions, policies addressing violence against women and mental health conditions in each country and the services available to women exposed to violence and women with mental health conditions. Results Forty-six per cent of women in Afghanistan, 21% of women in India and 5% of women in Sri Lanka reported experiencing physical violence within the last 12 months of the most recent survey. Meanwhile, 7% of ever-partnered women in Afghanistan, 6% of women in India and 7% of women in Sri Lanka reported experiencing sexual violence during their lifetime. In India, 6.9% of disability-adjusted life-years were attributed to childhood sexual abuse and 4.6% to intimate partner violence. In Sri Lanka, 14.6% of women exposed to physical or sexual violence by a partner had engaged in self-harm. We found no data on conflict-related sexual violence and trafficking. All three countries have made commitments to gender equality or preventing violence against women. Implementation of some of these policies, however, is unclear. The countries also have had mental health policies and services, but there is currently little intersection between mental health and violence against women. Limitations The situation analysis is limited by the data available and the generalisability of findings. Conclusion The three countries have limited data, policies and legislation on the intersection between all forms of violence against women and poor mental health as well as a paucity of mental health service provision. Future work Future research should focus on integrating mental health care within social services; translating trauma-informed approaches into service provision and addressing family violence within violence against women. Funding This article presents independent research funded by the National Institute for Health and Care Research (NIHR) Global Health Research programme as award number 17/63/47.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".