MétaCan
Menu
Back to cohort
Record W4280629826 · doi:10.1177/15412040221089235

Gender Differences in the Prevalence and Predictive Validity of Protective Factors in a Sample of Justice-Involved Youth

2022· article· en· W4280629826 on OpenAlexaffabout
Julie Goodwin, Shelley L. Brown, Tracey A. Skilling

Bibliographic record

VenueYouth Violence and Juvenile Justice · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsCentre for Addiction and Mental HealthUniversity of TorontoCarleton University
Fundersnot available
KeywordsRecidivismPredictive validityPsychologyEconomic JusticeOccupational safety and healthInjury preventionPoison controlHuman factors and ergonomicsClinical psychologySuicide preventionDemographyMedicineEnvironmental healthPolitical scienceSociology

Abstract

fetched live from OpenAlex

Research on strengths and violent behavior in justice-involved youth suggests that the prevalence and predictive validity of strength factors vary as a function of gender. Interviews conducted between 2009 and 2012 with 185 justice-involved Canadian youth ( N female = 84, N male = 101; 67% violent index offence) were coded retrospectively using two strength measures for violence prediction: the protective domain of the Structured Assessment of Violence Risk in Youth (SAVRY), and the Structured Assessment of Protective Factors-Youth Version (SAPROF-YV). Males exhibited more protective factors than females across measures. Both tools were strong predictors of general recidivism in males but not females. The SAVRY protective domain was predictive of violent recidivism in males, but the SAPROF-YV was not; neither was predictive of violent recidivism in females. This study demonstrates gender differences in the prevalence and predictive validity of strengths in justice-involved youth and highlights the need for more female-focused research and measures.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.150
Threshold uncertainty score0.906

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.086
GPT teacher head0.312
Teacher spread0.226 · 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

Citations9
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueYouth Violence and Juvenile JusticeSame topicIntimate Partner and Family ViolenceFrench-language works237,207