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Record W4411507132 · doi:10.1080/10911359.2025.2523900

Knowledge and perception of wives’ violence against husbands in Northern Ghana

2025· article· en· W4411507132 on OpenAlexaff
Paul Alhassan Issahaku, Akanganngang Joseph Asitik

Bibliographic record

VenueJournal of Human Behavior in the Social Environment · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsPerceptionPsychologyDomestic violenceSocial psychologyCriminologyHuman factors and ergonomicsPoison controlMedical emergencyMedicine

Abstract

fetched live from OpenAlex

This study contributes to existing literature by reporting on knowledge and perception of wives’ intimate partner violence (IPV) perpetration in Ghana using a sample of 310 college respondents from Northern Ghana. Overall, 90% of the sample viewed wives as perpetrators of IPV. Wives are viewed as perpetrators of physical violence (73%), psychological violence (83%), and sexual violence (85%). Further, a majority of participants know wives who have perpetrated physical violence (66%), psychological violence (76%), and sexual violence (58%). An ANOVA results showed that male participants rated wives’ sexual violence (M = 5.11, SD = 2.88) and physical violence (M = 4.65, SD = 2.62) very serious compared to female participants (M = 3.84, SD = 2.75) and (M = 3.89, SD = 2.67), respectively. Further, participants in Year 3 (M = 5.07, SD = 2.81) rated wives’ psychological violence very serious compared to those in Year 2 (M = 3.84, SD = 2.47), and participants in Year 4 rated wives’ psychological violence very serious compared to those in Year 2 (M = 5.11, SD = 2.95). Several interpersonal, intrapersonal, economic, and socio-cultural factors are identified as reasons for wives’ IPV. Alongside efforts to curb men’s IPV against women, wives’ violence against husbands should be acknowledged and addressed by policymakers and practitioners.

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)0.001
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 score0.201
Threshold uncertainty score0.273

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.025
GPT teacher head0.345
Teacher spread0.321 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Human Behavior in the Social EnvironmentSame topicIntimate Partner and Family ViolenceFrench-language works237,207