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Record W2895323384 · doi:10.1177/1524838018801335

Ethnoregional Dynamics of Intimate Partner Violence Against Women in Nigeria

2018· review· en· W2895323384 on OpenAlexaffabout
Eugene Emeka Dim

Bibliographic record

VenueTrauma Violence & Abuse · 2018
Typereview
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsDomestic violenceIgboHausaEthnic groupResidenceYorubaGender studiesPopulationInclusion (mineral)NationalitySociologyPsychologyPoison controlGeographySuicide preventionMedicineDemographyImmigrationAnthropology

Abstract

fetched live from OpenAlex

Intimate partner violence (IPV) is a reality the average Nigerian woman has to grapple with everyday. Ethnicity and place of residence play a pivotal role in the social realities of Nigerian women, especially with their experience of IPV. However, there is a paucity of reviews examining the impact of ethnicity and place of residence on women’s experiences of IPV in Nigeria. This study seeks to explore the ethnoregional dynamics of IPV from the range of studies undertaken on the subject matter. Eighteen studies meant the inclusion criteria for analysis. The major criteria for selecting studies for analysis were peer-reviewed studies on IPV against women and studies on a Nigerian population, regardless of the nationality of the authors. Databases like the University of Saskatchewan Library , ProQuest , Sociological Abstracts , and Journal Storage ( JSTOR) were used in searching for peer-reviewed studies. The study revealed that Igbo women tend to experience IPV more than Yoruba and Hausa women. The study also found that rural women tend to experience IPV more than urban women. The study points out some of the services available for female victims of IPV and the possible strategies that can be adapted to reach them effectively.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.969
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.050
GPT teacher head0.366
Teacher spread0.315 · 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 designOther design
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

Citations10
Published2018
Admission routes2
Has abstractyes

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