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Record W4366528584 · doi:10.1177/00938548231165286

Assessment of Strengths in Criminal Justice System-Impacted Youth: A Retrospective Validation Study of the SAPROF-YV

2023· article· en· W4366528584 on OpenAlexafffundabout
Sonia Finseth, Michele Peterson‐Badali, Shelley L. Brown, Tracey A. Skilling

Bibliographic record

VenueCriminal Justice and Behavior · 2023
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsCentre for Addiction and Mental HealthCarleton UniversityUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsRecidivismModerationPsychologyReliability (semiconductor)Poison controlRisk assessmentInjury preventionClinical psychologyMedicineSocial psychologyMedical emergencyComputer securityComputer science

Abstract

fetched live from OpenAlex

The Structured Assessment of Protective Factors for Violence Risk-Youth Version (SAPROF-YV; de Vries Robbé et al., 2015) was designed specifically to assess strengths as a complement to risk assessment tools. We retrospectively examined its reliability and validity in 305 Canadian community-sentenced youth, both in the overall sample and in male and female, and Black and White, subgroups. In all groups, the total score had strong internal consistency, inter-rater reliability, and convergent validity, and significantly predicted general recidivism at 3-year fixed follow-up. The SAPROF-YV showed incremental validity over the YLS/CMI only in Black youth. In the total sample, a moderation effect was identified whereby strengths were protective at lower levels of risk but not for moderate or high risk youth. The SAPROF-YV shows promising reliability and validity; however, more research is needed before clear guidance can be provided regarding the use of this measure in clinical practice.

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)0.004
metaresearch head score (Gemma)0.007
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 score0.184
Threshold uncertainty score0.365

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.073
GPT teacher head0.397
Teacher spread0.324 · 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

Citations3
Published2023
Admission routes3
Has abstractyes

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