Nurses’ Role in Caring for Women Experiencing Intimate Partner Violence in the Sri Lankan Context
Bibliographic record
Abstract
Intimate partner violence has short- and long-term physical and mental health consequences. As the largest healthcare workforce globally, nurses are well positioned to care for abused women. However, their role in this regard has not been researched in some countries. This paper is based on a qualitative study that explored how Sri Lankan nurses perceive their role in caring for women who have experienced partner violence. Interviews with 30 nurses who worked in diverse clinical and geographical settings in Sri Lanka revealed that nurses' role involved: identifying abuse, taking care of patients' physical needs, attending to their safety, providing support and advice, and making referrals. Barriers to providing care included lack of knowledge; heavy workload; language barriers; threats to personal safety; nurses' status within the healthcare hierarchy; and lack of communication and collaboration between various stakeholder groups within the healthcare system. Nurses also identified a lack of appropriate services and support within hospitals and in the community. The findings reveal an urgent need for the healthcare system to respond to nurses' educational and training needs and help them function autonomously within multidisciplinary teams when caring for abused women. The findings also point to a need to address institutional barriers including the lack of appropriate services for abused women.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".