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Record W4387379063 · doi:10.1371/journal.pone.0291913

Ending violence against women: Help-seeking behaviour of women exposed to intimate partner violence in sub-Saharan Africa

2023· article· en· W4387379063 on OpenAlexaff
Richard Gyan Aboagye, Abdul‐Aziz Seidu, Abdul Cadri, Tarif Salihu, Francis Arthur-Holmes, Sarah Tara Sam, Bright Opoku Ahinkorah

Bibliographic record

VenuePLoS ONE · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsMcGill University
Fundersnot available
KeywordsDomestic violenceDemographyOdds ratioMedicineMarital statusSexual violenceConfidence intervalTanzaniaHelp-seekingPoison controlLogistic regressionInjury preventionPublic healthPopulationPsychologyPsychiatryEnvironmental healthMental healthGeography

Abstract

fetched live from OpenAlex

INTRODUCTION: Intimate partner violence is a serious public health problem that transcends cultural boundaries in sub-Saharan Africa. Studies have reported that violence characteristics and perception are strong predictors of help-seeking among women. We assessed the prevalence and factors associated with help-seeking among female survivors of intimate partner violence in sub-Saharan Africa. METHODS: We pooled data from the most recent Demographic and Health Surveys (DHS) of eighteen sub-Saharan African countries. The data were extracted from the women's files in countries with datasets from 2014 to 2021. A weighted sample of 33,837 women in sexual relationships: married or cohabiting who had ever experienced intimate partner violence within the five years preceding the survey were included in the analysis. Percentages with 95% confidence interval (CI) were used to present the results of the prevalence of help-seeking for intimate partner violence. We used a multilevel binary logistic regression analysis to examine the factors associated with help-seeking among survivors of intimate partner violence. The results were presented using adjusted odds ratio (AOR) with their respective 95% CI. Statistical significance was set at p<0.05. RESULTS: Out of the 33,837 women who had ever experienced intimate partner violence in sub-Saharan Africa, only 38.77% (95% CI = 38.26-39.28) of them sought help. Ethiopia had the lowest prevalence of women who sought help after experiencing intimate partner violence (19.75%; 95% CI = 17.58-21.92) and Tanzania had the highest prevalence (57.56%; 95% CI = 55.86-59.26). Marital status, educational level, current working status, parity, exposure to interparental violence, women's autonomy in household decision-making, mass media exposure, intimate partner violence justification, wealth index, and place of residence were associated with help-seeking behaviour of intimate partner violence survivors. CONCLUSION: The low prevalence of help seeking among women who have experienced intimate partner violence in sub-Saharan Africa calls for the intensification of formal and informal sources of assistance. Education can play a critical role in empowering girls, which may increase future help-seeking rates. Through media efforts aimed at parental awareness, the long-term benefits of females enrolling in school could be achieved. However, concentrating solely on individual measures to strengthen women's empowerment may not bring a significant rise in help-seeking as far as patriarchal attitudes that permit violence continue to exist. Consequently, it is critical to address intimate partner violence from the dimensions of both the individual and violence-related norms and attitudes. Based on the findings, there should be public awareness creation on the consequences of intimate partner violence. Respective governments must increase their coverage of formal support services to intimate partner violence survivors especially those in rural communities.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.061
GPT teacher head0.295
Teacher spread0.234 · 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 source (direct Gemma or distilled Codex), 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

Citations25
Published2023
Admission routes1
Has abstractyes

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