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Record W2166915228 · doi:10.1177/1541204010371793

Comparative Analyses of the YLS/CMI, SAVRY, and PCL:YV in Adolescent Offenders: A 10-year Follow-Up Into Adulthood

2010· article· en· W2166915228 on OpenAlexaff
Fred Schmidt, Mary Ann Campbell, Carolyn Houlding

Bibliographic record

VenueYouth Violence and Juvenile Justice · 2010
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsUniversity of New BrunswickLakehead University
Fundersnot available
KeywordsRecidivismPsychopathy ChecklistPredictive validityIncremental validityPsychologyChecklistRisk assessmentPoison controlInjury preventionClinical psychologyTest validityPsychometricsMedicineMedical emergencyAntisocial personality disorderComputer security

Abstract

fetched live from OpenAlex

A growing body of research has been dedicated to developing adolescent risk assessment instruments, but much of this research has been limited to short-term tests of predictive validity. The current study examined the predictive and incremental validity of the Youth Level of Service/ Case Management Inventory (YLS/CMI), Structured Assessment of Violence Risk in Youth (SAVRY), and Psychopathy Checklist: Youth Version (PCL:YV) in adolescent offenders over a mean 10-year follow-up period. Each instrument predicted general recidivism with moderate- (YLS/CMI area under the curve [AUC] = .66) -to-large effect sizes (SAVRY AUC = .74; PCL:YV AUC = .79). However, there was variation in predictive validity across types of recidivism, and all three instruments were better at predicting recidivism in males than females. SAVRY total also demonstrated incremental validity over its structured professional judgment of risk. Clinical implications and future directions for youth risk assessment 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)0.002
metaresearch head score (Gemma)0.008
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 score0.021
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.072
GPT teacher head0.361
Teacher spread0.289 · 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

Citations152
Published2010
Admission routes1
Has abstractyes

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