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Record W2320831334

Prevalence and Correlates of Physical\nDating Violence Among North American\nIndigenous Adolescents

2015· article· W2320831334 on OpenAlexaboutno aff
Dane Hautala, Kelley J. Sittner, Brian E. Armenta, Les B. Whitbeck

Bibliographic record

VenueInsecta mundi · 2015
Typearticle
Language
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsnot available
Fundersnot available
KeywordsDemographyPoison controlPopulationAggressionInjury preventionSuicide preventionPsychologyDating violenceHuman factors and ergonomicsIndigenousOddsAngerOccupational safety and healthLogistic regressionDomestic violenceClinical psychologySocial psychologyMedicineEnvironmental healthSociology
DOInot available

Abstract

fetched live from OpenAlex

This study examined the lifetime prevalence of physical dating violence, including victimization, perpetration, and the overlap between the two (mutual violence), among a population sample of 551 reservation/reserve residing Indigenous (i.e., American Indian and Canadian First Nations) adolescents in the upper-Midwest of the United States and Canada. Potential correlates of four dating violence profiles (i.e., no dating violence, perpetration only, victimization only, and mutual violence) relevant to this population also were considered. The clearest pattern to emerge from multinomial logistic regression analyses suggested that adolescents who engage in problem behaviors, exhibit high levels of anger, and perceive high levels of discrimination have increased odds of lifetime mutual dating violence relative to those reporting no dating violence. Furthermore, gender comparisons indicated that females were more likely to report being perpetrators only, whereas males were more likely to report being victims only. Considerations of dating violence profiles and culturally relevant prevention strategies are discussed.

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)0.001
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 score0.033
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.022
GPT teacher head0.291
Teacher spread0.269 · 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

Citations18
Published2015
Admission routes1
Has abstractyes

Explore more

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