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Record W3213878093 · doi:10.1177/08862605211055145

The Role of Pornography Use in Intimate Partner Violence in Different-Sex Couples: A Prospective Longitudinal Study

2021· article· en· W3213878093 on OpenAlexaffabout
Katherine Jongsma, Patti Timmons Fritz

Bibliographic record

VenueJournal of Interpersonal Violence · 2021
Typearticle
Languageen
FieldPsychology
TopicSexuality, Behavior, and Technology
Canadian institutionsUniversity of WindsorUniversity Health Network
Fundersnot available
KeywordsPornographyDomestic violencePsychologyPoison controlLongitudinal studyBaseline (sea)Injury preventionSuicide preventionDevelopmental psychologyHuman factors and ergonomicsSocial psychologyClinical psychologyMedicineMedical emergency

Abstract

fetched live from OpenAlex

Pornography use and intimate partner violence (IPV) are both prevalent in romantic relationships. However, information is lacking about whether pornography use predicts IPV. This study examined the relation between frequency of pornography use (FPU) and IPV across a span of 4 months in a sample of 132 different-sex couple dyads. At least one partner in each couple was attending a Canadian university. Participants ( N = 264) completed online measures of pornography use, IPV, and social desirability at baseline and at a 4-month follow-up. Two longitudinal actor–partner interdependence models using a structural equation framework to conduct path analyses demonstrated that (a) higher FPU among men at baseline predicted increases in IPV perpetration and victimization from baseline to 4-month follow-up for both men and women and (b) women’s baseline FPU did not predict change in IPV over time for themselves or their partners. These findings suggest that frequent pornography use among male partners in different-sex romantic relationships may represent an under-recognized risk factor for IPV, and further research is needed to identify latent factors that may be contributing to this relation. Although women’s baseline FPU did not predict changes in IPV over time, this may be because women used pornography less frequently than men.

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)0.000
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 score0.045
Threshold uncertainty score0.699

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
Research integrity0.0000.001
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 head0.033
GPT teacher head0.344
Teacher spread0.311 · 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

Citations12
Published2021
Admission routes2
Has abstractyes

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