Assessment of Multiple Risk Outcomes, Strengths, and Change with the START:AV: A Short-Term Prospective Study with Adolescent Offenders
Bibliographic record
Abstract
The Short-Term Assessment of Risk and Treatability: Adolescent Version (START:AV; Nicholls, Viljoen, Cruise, Desmarais, & Webster, 2010; Viljoen, Cruise, Nicholls, Desmarais, & Webster, in preparation) is a clinical guide designed to assist in the assessment and management of adolescents' risk for adverse events (e.g., violence, general offending, suicide, victimization). In this initial validation study, START:AV assessments were conducted on 90 adolescent offenders (62 male, 28 female), who were prospectively followed for a 3-month period. START:AV assessments had good to excellent inter-rater reliability and strong concurrent validity with Structured Assessment of Violence Risk in Youth assessments (SAVRY; Borum, Bartel, & Forth, 2006). START:AV risk estimates and Vulnerability total scores predicted multiple adverse outcomes, including violence towards others, offending, victimization, suicidal ideation, and substance abuse. In addition, Strength total scores inversely predicted violence, offending, and street drug use. During the 3-month follow-up, risk estimates changed in at least one domain for 92% of youth, and 27% of youth showed reliable changes in Strength and/or Vulnerability total scores (reliable change index, 90% confidence interval; Jacobsen & Truax, 1991). While these findings are promising, a strong need exists for further research on the START:AV, the measurement of change, and on the role of strengths in risk assessment and treatment-planning.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".