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Record W2081966327 · doi:10.1177/0886260506290201

Prevalence and Predictors of Dating Violence Among Adolescent Female Victims of Child Sexual Abuse

2006· article· en· W2081966327 on OpenAlexaff
Mireille Cyr, Pierre McDuff, John Wright

Bibliographic record

VenueJournal of Interpersonal Violence · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsDating violenceSexual abusePoison controlInjury preventionSuicide preventionDomestic violenceSexual violencePsychologyHuman factors and ergonomicsClinical psychologyOccupational safety and healthSexual intercourseChild abuseMedicinePsychiatryPopulationMedical emergencyCriminology

Abstract

fetched live from OpenAlex

The purpose of this study was to advance knowledge of dating violence behaviors among adolescent victims of child sexual abuse (CSA), first, by determining the prevalence of psychological and physical dating violence and the reciprocity of violence, and second, by investigating the influence of certain CSA characteristics to dating violence. Respondents included 126 females ages 13 to 17 years. More than 45% reported experiencing some sort of physical violence in their dating relationships. Psychological violence was reciprocal in more than 90% of the cases. Multiple regression analysis revealed a significant contribution of CSA characteristics. Multiple regression analyses revealed that the duration of the sexual abuse and the presence of violence or completed intercourse during the abuse could significantly contribute to dating violence above and beyond other known risk factors. Discussion underscores the need to gain a better understanding of CSA and other risk factors that might influence violent dating behaviors.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.274
Teacher spread0.263 · 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

Citations104
Published2006
Admission routes1
Has abstractyes

Explore more

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