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Record W2291911013 · doi:10.3390/bs6010007

Dating Violence among High-Risk Young Women: A Systematic Review Using Quantitative and Qualitative Methods

2016· review· en· W2291911013 on OpenAlexaff
Lauren Joly, Jennifer Connolly

Bibliographic record

VenueBehavioral Sciences · 2016
Typereview
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsYork University
Fundersnot available
KeywordsDating violenceModerationPsychologyDomestic violenceMental healthVulnerability (computing)Suicide preventionPoison controlQualitative researchOccupational safety and healthInjury preventionClinical psychologyPhysical abuseMedicinePsychiatryEnvironmental healthSocial psychologySociologyComputer security

Abstract

fetched live from OpenAlex

Our systematic review identified 21 quantitative articles and eight qualitative articles addressing dating violence among high risk young women. The groups of high-risk young women in this review include street-involved, justice-involved, pregnant or parenting, involved with Child Protective Services, and youth diagnosed with a mental health issue. Our meta-analysis of the quantitative articles indicated that 34% (CI = 0.24-0.45) of high-risk young women report that they have been victims of physical dating violence and 45% (CI = 0.31-0.61) of these young women report perpetrating physical dating violence. Significant moderator variables included questionnaire and timeframe. Meta-synthesis of the qualitative studies revealed that high-risk young women report perpetrating dating violence to gain power and respect, whereas women report becoming victims of dating violence due to increased vulnerability.

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)0.022
metaresearch head score (Gemma)0.072
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.022
Threshold uncertainty score0.116

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.072
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0070.006
Bibliometrics0.0180.016
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.319
GPT teacher head0.590
Teacher spread0.270 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations28
Published2016
Admission routes1
Has abstractyes

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