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Record W2130106734 · doi:10.1080/03630242.2014.896444

Sexual Risk Factors Associated with Intimate Partner Violence Against Women in Rwanda: A Couples-Based Analysis

2014· article· en· W2130106734 on OpenAlexaff
Jeanne Françoise Kayibanda, Raphaël Bitéra, Éric Demers, Jocelyne Moisan, Michel Alary

Bibliographic record

VenueWomen & Health · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsInstitut National de Santé Publique du QuébecUniversité LavalCentre hospitalier universitaire de Québec
Fundersnot available
KeywordsDomestic violenceOdds ratioDemographyMedicineSexual violenceLogistic regressionConfidence intervalSex organSexual partnerPoison controlInjury preventionClinical psychologyPsychologyEnvironmental healthFamily medicineInternal medicineNursing

Abstract

fetched live from OpenAlex

The goal of this study was to measure the prevalence of intimate partner violence (IPV) against women and to examine the independent association of sexual risk factors of women and their husbands/partners with IPV. We used data from 2,169 couples from the 2005 Rwanda Demographic and Health Survey. Face-to-face interviews were conducted, and multiple logistic regression was used to calculate adjusted odds ratios (AOR) and 95% confidence intervals (CI). A total of 560 (29.2%), 415 (22.2%), and 233 (12.4%) women reported any physical, psychological, or sexual IPV, respectively. History of genital ulcer in women was significantly associated with psychological IPV (AOR: 2.77; 95% CI: 1.67-4.59), whereas history of genital ulcer reported by husbands/partners was significantly associated with sexual IPV (AOR 2.80, 95% CI: 1.08-7.29). The number of lifetime sexual partners of husbands was positively associated with increasing odds of their wives being exposed to psychological IPV (p = 0.025) and physical IPV (p = .017). In a representative sample of couples, husbands/partners' sexual risk factors were associated with IPV reported by their wives. Genital ulcer appeared to be a risk marker for the presence of IPV victimization/perpetration.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.024
GPT teacher head0.315
Teacher spread0.292 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations2
Published2014
Admission routes1
Has abstractyes

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