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Record W2810926575 · doi:10.1037/pas0000612

An evaluation of the predictive validity of the SAVRY and YLS/CMI in justice-involved youth with fetal alcohol spectrum disorder.

2018· article· en· W2810926575 on OpenAlexafffund
Kaitlyn McLachlan, Andrew L. Gray, Ronald Roesch, Kevin S. Douglas, Jodi L. Viljoen

Bibliographic record

VenuePsychological Assessment · 2018
Typearticle
Languageen
FieldMedicine
TopicPrenatal Substance Exposure Effects
Canadian institutionsSimon Fraser UniversityUniversity of Guelph
FundersSocial Sciences and Humanities Research Council of CanadaMichael Smith Health Research BCCanadian Foundation on Fetal Alcohol Research
KeywordsRecidivismPsycINFOPsychologyFetal Alcohol Spectrum DisorderPredictive validityClinical psychologyPsychological interventionCriminal justicePopulationPsychiatryMedicineMEDLINEEnvironmental healthPregnancyCriminology

Abstract

fetched live from OpenAlex

Despite the high prevalence of fetal alcohol spectrum disorder (FASD) in youth criminal justice settings, there is currently no research supporting the use of violence risk assessment tools in this population. This study examined the predictive validity of the Structured Assessment of Violence Risk in Youth (SAVRY) and the Youth Level of Service/Case Management Inventory (YLS/CMI) in justice-involved youth with FASD. Participants were 100 justice-involved youth (ages 12-23; 81% male), including 50 diagnosed with FASD and 50 without FASD or prenatal alcohol exposure. The SAVRY and YLS/CMI were prospectively coded based on interview and file review, with recidivism (both any and violent specifically) coded 1-year post-baseline assessment. Results provide preliminary support for the validity of the SAVRY and YLS/CMI in predicting recidivism in justice-involved youth with FASD. Higher ratings across SAVRY and YLS/CMI domains were found in youth with FASD, underscoring a critical need for assessments and interventions to buffer recidivism risk and address clinical needs. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

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)0.002
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 score0.004
Threshold uncertainty score0.278

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.071
GPT teacher head0.384
Teacher spread0.313 · 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

Citations26
Published2018
Admission routes2
Has abstractyes

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