Towards a Global Interdisciplinary Evidence-Informed Practice: Intimate Partner Violence in the Ethiopian Context
Bibliographic record
Abstract
Background. Intimate partner violence is a global health issue and is associated with a range of health problems for women. Nurses, as the largest health workforce globally, are well positioned to provide care for abused women. Objectives. This nursing-led interdisciplinary project was conducted to understand the current state of knowledge about intimate partner violence in Ethiopia and make recommendations for country-specific activities to improve response to intimate partner violence through practice changes, education, and research. Methods. The project involved two phases: review of relevant literature and an interdisciplinary stakeholder forum and a meeting with nurse educators. Findings. The literature review identified the pervasiveness and complexity of intimate partner violence and its sociocultural determinants in the Ethiopian context. Two significant themes emerged from the forum and the meeting: the value of bringing multiple disciplines together to address the complex issue of intimate partner violence and the need for health care professionals to better understand their roles and responsibilities in actively addressing intimate partner violence. Conclusions. Further research on the topic is needed, including studies of prevention and resilience and "best practices" for education and intervention. Interdisciplinary and international research networks can support local efforts to address and prevent intimate partner violence.
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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 | 0.291 | 0.237 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.026 | 0.015 |
| Open science | 0.006 | 0.025 |
| Research integrity | 0.013 | 0.016 |
| 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".