Nurses’ Knowledge, Attitudes, Practices and Associated Factors in the Care of Women Subjected to Intimate Partner Violence in the Western Province of Sri Lanka
Bibliographic record
Abstract
Intimate partner violence (IPV) is recognized as a preventable public health problem. Previous studies in Sri Lanka report high prevalence rates of IPV. Nurses as the largest healthcare force can take on a significant role in the care of women subjected to IPV. This study aimed to describe nurses’ knowledge, attitudes and practices related to providing care for women subjected to IPV in the Sri Lankan context. A cross-sectional study was conducted with 407 female nurses from 17 hospitals in the Western Province, using a stratified random sampling strategy. A pretested self-administered questionnaire was used. Most (85%) participants had poor overall knowledge related to IPV. Higher knowledge scores were found for: acts indicating IPV (80±28.2), health problems related to IPV (74.6±24.4), and reasons preventing disclosure (81.4±19.6). Knowledge scores were low for root causes of IPV (49.7±19.0), laws pertaining to IPV (31.0±25.9), and the available services (19.1±25.07). Good overall attitudes were evident among 54%, specifically, in the areas of inquiring about IPV (91%), offering the assistance (79.8%), and maintaining confidentiality (57%). Most (86.5%) had met women subjected to IPV, and the most frequent (52.3%) action they had taken was to inform a doctor. Higher levels of education, in-service learning, and learning about IPV in basic nursing education, were positively associated with knowledge and attitude levels. The results call for an urgent need for inclusion of IPV related content and skills-training in nursing curricula to enable nurses to identify, support, and provide care to women subjected to IPV.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".