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Record W2589256105 · doi:10.1080/17441692.2017.1293126

The ethics of researching intimate partner violence in global health: A case study from global health research

2017· article· en· W2589256105 on OpenAlexaff
Jeneviève Mannell, Adrian Guţă

Bibliographic record

VenueGlobal Public Health · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsUniversity of Windsor
FundersLondon School of Economics and Political Science
KeywordsDomestic violenceFraming (construction)Poison controlGlobal healthFocus groupQualitative researchOccupational safety and healthSuicide preventionSociologyPolitical scienceMedicinePublic relationsEconomic growthPublic healthEnvironmental healthNursingSocial scienceLawGeography

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.071
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Scholarly communication
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.442
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0710.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0130.003
Scholarly communication0.0010.001
Open science0.0030.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.359
GPT teacher head0.590
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2017
Admission routes1
Has abstractyes

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