Using dynamic risk and protective factors to predict inpatient aggression: Reliability and validity of START assessments.
Bibliographic record
Abstract
The Short-Term Assessment of Risk and Treatability (START; C. D. Webster, M. L. Martin, J. Brink, T. L. Nicholls, & S. L. Desmarais, 2009; C. D. Webster, M. L. Martin, J. Brink, T. L. Nicholls, & C. Middleton, 2004) is a relatively new structured professional judgment guide for the assessment and management of short-term risks associated with mental, substance use, and personality disorders. The scheme may be distinguished from other violence risk assessment instruments because of its inclusion of 20 dynamic factors that are rated in terms of both vulnerability and strength. This study examined the reliability and validity of START assessments in predicting inpatient aggression. Research assistants completed START assessments for 120 male forensic psychiatric patients through review of hospital files. They also completed Historical-Clinical-Risk Management-20 (HCR-20; C. D. Webster, K. S. Douglas, D. Eaves, & S. D. Hart, 1997) and Hare Psychopathy Checklist: Screening Version (PCL:SV; S. D. Hart, D. N. Cox, & R. D. Hare, 1995) assessments. Outcome data were coded from hospital files for a 12-month follow-up period using the Overt Aggression Scale (OAS; S. C. Yudofsky, J. M. Silver, W. Jackson, J. Endicott, & D. W. Williams, 1986). START assessments evidenced excellent interrater reliability and demonstrated both predictive and incremental validity over the HCR-20 Historical subscale scores and PCL:SV total scores. Overall, results support the reliability and validity of START assessments and use of the structured professional judgment approach more broadly, as well as the value of using dynamic risk and protective factors to assess violence risk.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".