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Record W2153801970 · doi:10.1177/0886260508322192

Mental Health Correlates of Intimate Partner Violence in Marital Relationships in a Nationally Representative Sample of Males and Females

2008· article· en· W2153801970 on OpenAlexaff
Tracie O. Afifi, Harriet L. MacMillan, Brian J. Cox, Gordon J. G. Asmundson, Murray B. Stein, Jitender Sareen

Bibliographic record

VenueJournal of Interpersonal Violence · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsMcMaster UniversityUniversity of ReginaUniversity of Manitoba
FundersNational Institute of Mental Health
KeywordsDomestic violenceMental healthPoison controlNational Comorbidity SurveySuicide preventionPsychiatryInjury preventionClinical psychologySuicidal ideationPsychologySexual abuseOccupational safety and healthMedicineHuman factors and ergonomicsMedical emergency

Abstract

fetched live from OpenAlex

It is important to understand the epidemiology of intimate partner violence (IPV) experienced by both males and females. Data were drawn from the U.S. National Comorbidity Survey Replication. The relationships between physical IPV and child abuse, mental disorders, and suicidal ideation and attempts among males and females were examined. The results indicate that child sexual abuse was associated with IPV among males, whereas child physical and sexual abuse was associated with IPV among females. IPV was associated with poor mental health outcomes for males and females, although sex differences are noted. The sex differences indicate that females experience a wider range of poor mental health outcomes compared to males. Knowledge about correlates of IPV can be useful in identifying individuals exposed to violence. Further research is required to identify effective methods to reduce exposure to IPV and to adequately address the specific needs of male and female victims of IPV.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.057
GPT teacher head0.357
Teacher spread0.299 · 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 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

Citations304
Published2008
Admission routes1
Has abstractyes

Explore more

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