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Record W4409625687 · doi:10.1177/10443894251322846

Intimate Partner Violence: A Qualitative Study of Male Victims’ Experiences With Their Natal and In-Law Families

2025· article· en· W4409625687 on OpenAlexaff
Stanley Oloji Isangha, Hau-lin Tam Cherry, Susan S. Chuang, Tosin Yinka Akintunde, Anna Choi, Ebenezer Cudjoe, Retsat Umar Dazang

Bibliographic record

VenueFamilies in Society The Journal of Contemporary Social Services · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsUniversity of AlbertaUniversity of Guelph
Fundersnot available
KeywordsDomestic violenceQualitative researchCriminologyPsychologyIntimate partnerFamily lawSocial psychologySociologyLawMedicineSuicide preventionPoison controlMedical emergencyPolitical science

Abstract

fetched live from OpenAlex

Research indicates that male victims of intimate partner violence (IPV) are often reluctant to seek help. When they do, they frequently turn to their informal support networks, such as natal and in-law families, rather than formal societal resources. In Nigeria, where the influence of these families is particularly strong, there is a notable absence of studies exploring how supportive these familial relationships are for heterosexual male victims. To address this critical gap in the literature, we conducted semi-structured interviews with 35 Nigerian heterosexual male victims of IPV. Using content analysis within a grounded theory framework, we identified four key themes. Our findings indicated that regardless of whether natal and in-law families were perceived as supportive or unsupportive, victims experienced negative emotional consequences. Based on these insights, we offer targeted recommendations and implications for both practice and research.

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)0.005
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0110.006
Scholarly communication0.0040.004
Open science0.0020.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.022
GPT teacher head0.345
Teacher spread0.323 · 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 designQualitative
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

Citations1
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueFamilies in Society The Journal of Contemporary Social ServicesSame topicIntimate Partner and Family ViolenceFrench-language works237,207