Taking Stock and Taking Steps: The Case for an Adolescent Version of the Short-Term Assessment of Risk and Treatability
Bibliographic record
Abstract
The field of violence risk assessment has matured considerably, possibly advancing beyond its own adolescence. At this point in the field's evolution, it is more important than ever for the development of any new device to be accompanied by a strong rationale and the capacity to provide a unique contribution. With this issue in mind, we first take stock of the field of adolescent risk assessment in order to describe the rapid progress that this field has made, as well as the gaps that led us to adapt the Short-Term Assessment of Risk and Treatability (START; Webster, Martin, Brink, Nicholls, & Desmarais, 2009) for use with adolescents. We view the Short-Term Assessment of Risk and Treatability: Adolescent Version (START:AV; Nicholls, Viljoen, Cruise, Desmarais, & Webster, 2010; Viljoen, Cruise, Nicholls, Desmarais, & Webster, in progress) as complementing other risk measures in four primary ways: 1) rather than focusing solely on violence risk, it examines broader adverse outcomes to which some adolescents are vulnerable (including self-harm, suicide, victimization, substance abuse, unauthorized leave, self-neglect, general offending); 2) it places a balanced emphasis on adolescents' strengths; 3) it focuses on dynamic factors that are relevant to short-term assessment, risk management, and treatment planning; and 4) it is designed for both mental health and justice populations. We describe the developmentally-informed approach we took in the adaptation of the START for adolescents, and outline future steps for the continuing validation and refinement of the START:AV.
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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 | 0.120 | 0.166 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.039 |
| Scholarly communication | 0.014 | 0.028 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.010 | 0.043 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".