Dynamic violence risk, protective factors, and therapeutic change in a gender and ethnoculturally diverse sample of court-adjudicated youth.
Bibliographic record
Abstract
The present study examined the predictive properties of three youth forensic measures-the Violence Risk Scale-Youth Version (VRS-YV), Structured Assessment of Violence Risk in Youth (SAVRY), and the Structured Assessment of Protective Factors-Youth Version (SAPROF-YV)-in a diverse court-adjudicated sample of 257 youth referred for assessment and intervention services at an outpatient mental health facility, and followed up an average of 9.4 years in the community. Study measures were rated from court and clinical files, along with treatment participation, and recidivism outcome data were obtained from official criminal records. The three measures had strong interrater and convergent validity, and moderate to high predictive accuracy for violent, nonviolent, and general recidivism. The measures significantly predicted outcome across male, female, Indigenous, and non-Indigenous groups; however, prediction magnitudes showed some variability with respect to specific risk/protection domains and outcome types. Cox regression survival analyses demonstrated incremental predictive validity for each violence risk measure, but not protection measures, with respect to each of the three recidivism outcomes. Moreover, pre-/posttreatment measurements of change on the VRS-YV dynamic factors were significantly associated with decreased nonviolent recidivism, controlling for baseline risk and protection. Violence risk (VRS-YV) and protection (SAPROF-YV) scores have the potential to be integrated in meaningful ways to capture the potential risk-mitigating effects of protective factors. Implications for integrating risk, protection, and treatment change information in clinical-forensic service delivery to diverse and violent youth populations are discussed. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".