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Record W2107304908 · doi:10.1177/0093854813496999

The Validity and Reliability of the Violence Risk Scale–Youth Version in a Diverse Sample of Violent Young Offenders

2013· article· en· W2107304908 on OpenAlexaff
Keira C. Stockdale, Mark E. Olver, Stephen C. P. Wong

Bibliographic record

VenueCriminal Justice and Behavior · 2013
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsRecidivismPsychologyClinical psychologyPoison controlInjury preventionPredictive validityScale (ratio)Intraclass correlationHuman factors and ergonomicsSuicide preventionReliability (semiconductor)Occupational safety and healthPsychiatryPsychometricsMedicineMedical emergency

Abstract

fetched live from OpenAlex

The Violence Risk Scale–Youth Version (VRS-YV; S. Wong, Lewis, Stockdale, & Gordon, 2004-2011) is a risk assessment and treatment planning tool for youths designed to assess violence risk, identify dynamic risk factors or treatment targets, and evaluate changes in risk from treatment or other change agents. We examined the psychometric properties of the VRS-YV on a diverse sample of 147 young offenders. The tool demonstrated high internal consistency (α = .90) and interrater reliability (intraclass correlation coefficient [ICC] = .90). Exploratory factor analysis (EFA) identified three factors: Interpersonal Aggression, Antisocial Tendencies, and Family Problems. VRS-YV static, dynamic, and total scores significantly predicted violent and general recidivism, including youth and adult outcomes, with moderate to high accuracy (area under the curve [AUC] = .65-.77); however, results varied among ethnic/cultural, gender, and developmental subgroups. The VRS-YV also demonstrated strong convergent validity with two well-established youth forensic assessment tools. Clinical implications of these findings and future research directions are discussed in this article.

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 score0.098
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.043
GPT teacher head0.306
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 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

Citations43
Published2013
Admission routes1
Has abstractyes

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