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Record W4406950378 · doi:10.1177/00938548241307235

The Intersection of Juvenile Psychopathy, Protective Factors, Treatment Change, and Diversity in Justice-Involved Youth

2025· article· en· W4406950378 on OpenAlexaffabout
Kristine May Lovatt, Keira C. Stockdale, Mark E. Olver

Bibliographic record

VenueCriminal Justice and Behavior · 2025
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsUniversity of SaskatchewanMinistry of Community Safety and Correctional Services
Fundersnot available
KeywordsPsychopathyJuvenileIntersection (aeronautics)Diversity (politics)Poison controlEconomic JusticeHuman factors and ergonomicsPsychologyInjury preventionSuicide preventionJuvenile delinquencyCriminologyDevelopmental psychologySocial psychologyEnvironmental healthMedicineTransport engineeringEngineeringPolitical scienceEcologyPersonalityBiology

Abstract

fetched live from OpenAlex

We examined the intersection of juvenile psychopathy with protective factors, dynamic violence risk, and recidivism in a court adjudicated Canadian sample of 257 male and female, ethnoracially diverse youth. The Psychopathy Checklist: Youth Version (PCL:YV) and measures of dynamic violence risk (Violence Risk Scale–Youth Version; VRS-YV) and protective factors (Structured Assessment of Protective Factors–Youth Version; SAPROF-YV) were rated from court and treatment files. Recidivism information was obtained from official criminal records. PCL:YV scores were associated with fewer protective factors and predicted recidivism across gender and ethnoracial (Indigenous vs non-Indigenous) groups; however, PCL:YV scores did not incrementally predict future crime and violence when controlling for violence risk and/or protective factors scores, while both of the latter measures did. Juvenile psychopathy is a clinically relevant construct for justice-involved youth but it does not equate to a lack of protective factors or inability to make treatment-related changes in risk.

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)0.000
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 score0.261
Threshold uncertainty score0.766

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.133
GPT teacher head0.357
Teacher spread0.224 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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