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Record W3217276571 · doi:10.1177/0192513x211042847

Help-Seeking Behaviors of Male Survivors of Intimate Partner Violence in Kenya

2021· article· en· W3217276571 on OpenAlexaff
Eric Y. Tenkorang, Mariama Zaami, Sitawa R. Kimuna, Adobea Yaa Owusu, Emmanuel Rohn

Bibliographic record

VenueJournal of Family Issues · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsDomestic violenceHelp-seekingMultinomial logistic regressionOddsPsychologyPoison controlClinical psychologySuicide preventionSocial psychologyLogistic regressionMedicinePsychiatryEnvironmental healthMental health

Abstract

fetched live from OpenAlex

Very few studies examine the help-seeking behaviors of male survivors of intimate partner violence (IPV) in Kenya or sub-Saharan Africa more generally. Using nationally representative cross-sectional data from 1,458 male survivors and multinomial logit models, we examined what influences men’s decision to seek help after experiencing IPV. Results show the majority of male survivors did not seek help. Those who did so turned to informal rather than formal sources. The severity of physical violence was the most robust and consistent predictor of help-seeking. Male survivors of severe physical abuse had higher odds of seeking help from informal support networks than not seeking help. Compared to the uneducated, highly educated men were significantly more likely to seek help from formal support networks than to not seek help at all. Sensitization programs are required to educate male survivors of IPV on available sources of support. In particular, barriers to help-seeking must be removed to encourage male survivors to find support.

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)0.002
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 score0.040
Threshold uncertainty score0.525

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.038
GPT teacher head0.361
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 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

Citations20
Published2021
Admission routes1
Has abstractyes

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