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Record W2901989665 · doi:10.4314/ahs.v18i4.47

Women’s attitude and reasons toward justifying domestic violence in Ethiopia: a systematic review and meta-analysis

2018· review· en· W2901989665 on OpenAlexaboutno aff
Yonas Deressa Guracho, Berhanu Boru Bifftu

Bibliographic record

VenueAfrican Health Sciences · 2018
Typereview
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsnot available
Fundersnot available
KeywordsDomestic violenceMeta-analysisMedicineWifePoison controlInjury preventionSystematic reviewSuicide preventionHuman factors and ergonomicsDemographyEnvironmental healthMEDLINELawPolitical scienceSociology

Abstract

fetched live from OpenAlex

BACKGROUND: Domestic violence (DV) is a global public problem that touches all levels of society and socio-economic status. Identifying women's attitudes towards domestic violence is an important first step in the prevention and control of its consequence. Thus, this systematic review and meta-analysis aimed: (i) to synthesize women's reasons for justifying domestic violence and (ii) to determine the pooled prevalence of women's attitude towards domestic violence in Ethiopia. METHODS: Pub-Med and google scholar data bases searched for quantitative cross-sectional studies. The study quality was assessed with the Newcastle-Ottawa quality assessment tool. Heterogeneity test and evidence of publication bias were assessed. Pooled prevalence of women's attitude was calculated with 95%CI using random effects model. RESULTS: A total of 15 articles were included in the study. The pooled prevalence of women's attitude towards justifying domestic violence was found to be 57% (95% CI; 47.0%-67.2%). Reasons for justifying were: burning food, argues with husband, goes out without telling, neglects children, refuses sex, unfaithful, disobeys and suspects infidelity. CONCLUSION: More than half of women accept domestic violence. Authors' suggest strengthening of women's awareness toward norms that justify wife beating.

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)0.016
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.590
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0070.001
Bibliometrics0.0010.005
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.226
GPT teacher head0.473
Teacher spread0.247 · 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.

Study designSystematic review
Domainnot available
GenreReview

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

Citations24
Published2018
Admission routes1
Has abstractyes

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