A review of the Sri Lankan health-sector response to intimate partner violence: looking back, moving forward
Bibliographic record
Abstract
Intimate partner violence (IPV) is a major health concern for women worldwide. Prevalence rates for IPV are high in the World Health Organization South-East Asia Region, but little is known about health-sector responses in this area. Health-care professionals can play an important role in supporting women who are seeking recourse from IPV. A comprehensive search was conducted to identify relevant published and grey literature over the last 35 years that focused on IPV, partner/ spousal violence, wife beating/abuse/battering, domestic violence, and Sri Lanka. Much of the information about current health-sector response to IPV in Sri Lanka was not reported in published and grey literature. Therefore, key personnel from the Ministry of Health, hospitals, universities and nongovernmental organizations were also interviewed to gain additional, accurate and timely information. It was found that the health-sector response to IPV in Sri Lanka is evolving, and consists of two models of service provision: (i) gender based violence desks, which integrate selective services at the provider/facility level; and (ii) Mithuru Piyasa (Friendly Abode) service points, which integrate comprehensive services at the provider/facility level and some at the system level. This paper presents each model's strengths and limitations in providing comprehensive and integrated health services for women who experience IPV in the Sri Lankan context.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".