The ethics of researching intimate partner violence in global health: A case study from global health research
Bibliographic record
Abstract
There has been an increase in attention to intimate partner violence (IPV) as a health issue that contributes to the spread of HIV, physical and emotional stress, depression, substance use, serious injuries, and higher rates of mortality in low-income settings. This paper explores the ethical implications raised by research on IPV by global health scholars. Drawing on Hedgecoe's work on critical bioethics to analyse a qualitative study of IPV in Rwanda, this paper discusses the risks and benefits of conducting research on IPV as part of the global health agenda. We discuss ethical issues that have become evident through our work in this area, including: raising IPV as an issue of concern in women's lives in settings where economic support for women experiencing IPV may not exist; recording interviews and focus group discussions in contexts with significant government surveillance; ethical tensions in appropriating local voices in ways that position women as 'victims' of violence; and the risks associated with framing IPV as a global health issue separate from feminist advocacy. We recommend more tailored approaches to ethics in IPV research, which considers the specificity of the social, cultural and economic 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 | 0.054 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.024 | 0.031 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.008 | 0.008 |
| 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".