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Record W2897252523 · doi:10.1177/0269758018799030

A multilevel analysis of contextual risk factors for intimate partner violence in Ghana

2018· article· en· W2897252523 on OpenAlexaff
Nicholas Cofie

Bibliographic record

VenueInternational Review of Victimology · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsQueen's University
Fundersnot available
KeywordsSocioeconomic statusDomestic violencePoison controlContext (archaeology)Multilevel modelPsychological interventionPopulationHuman factors and ergonomicsPsychologySuicide preventionOccupational safety and healthSocioeconomicsInjury preventionSocial psychologyEnvironmental healthGeographySociologyMedicinePsychiatry

Abstract

fetched live from OpenAlex

While extant research suggests that context, structural socioeconomic and cultural factors matter in intimate partner violence research, quantitative research on the subject in sub-Saharan Africa, and in particular Ghana, has disproportionately focused on prevalence and individual level correlates of spousal violence. This research has ignored the role of the structural socioeconomic and cultural factors and contexts in understanding the causes and consequences of spousal violence in a setting where family life is heavily influenced by traditional norms and beliefs. These norms and beliefs may lead to inadequate and ineffective interventions geared at preventing or reducing spousal violence and its consequences. Guided by an integrated theoretical approach, this study addresses these issues by estimating a multilevel logistic regression model where the effects of both individual and community level risk factors for spousal violence are assessed. Data for the study come from the Ghana Demographic and Health Survey and the Ghana Population and Housing Census. The findings confirm the salient role of structural socioeconomic and cultural factors, such as patriarchal norms and residential instability, in the perpetration of spousal violence against women. Policy implications of these findings and directions for further research are discussed.

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)0.002
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 score0.278
Threshold uncertainty score0.772

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.042
GPT teacher head0.407
Teacher spread0.365 · 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

Citations14
Published2018
Admission routes1
Has abstractyes

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