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 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 | 0.003 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".