The Role of Client Strengths in Assessments of Violence Risk Using the Short-Term Assessment of Risk and Treatability (START)
Bibliographic record
Abstract
Despite significant advances in the field of violence risk assessment, we are limited in our understanding regarding the utility of existing measures for predicting violence risk over brief time frames (i.e., weeks to months) as well as by our focus on factors that increase risk to the neglect of those which may reduce risk or protect against future violence. To address these knowledge gaps, this study evaluated the use of a structured professional guide, the Short-Term Assessment of Risk and Treatability (START; Webster, Martin, Brink, Nicholls, & Middleton, 2004), in assessing short-term violence risk (i.e., up to one year) and, specifically, the role of client strengths in this process. Research assistants completed file-based START assessments for four 3-month intervals for 30 male forensic psychiatric inpatients. Information pertaining to aggressive incidents was obtained from files. Overall, results supported the usefulness of the START in assessing short-term violence risk. Assessments evidenced validity in predicting future violence, particularly over the short-term (i.e., up to 9 months). Although ratings of client strengths did not contribute uniquely to the prediction of violence risk, results support their clinical utility in risk management.
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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 | 0.016 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".