A Clinical and Risk Profile of Forensic Psychiatric Patients: Treatment Team STARTs in a Canadian Service
Bibliographic record
Abstract
Current best practice guidelines recommend clinicians consider clients’ strengths as well as their deficits (APA, 2006; Department of Health, 2007). The Short-Term Assessment of Risk and Treatability (START) is one of the few structured professional judgment (SPJ) measures that facilitate this balanced approach to assessment and treatment planning. START is a concise clinical guide for the assessment and management of seven short-term (i.e., weeks to months) risk estimates (violence, self-harm, suicide, substance abuse, unauthorized leave, self-neglect, and being victimized) that occur at elevated rates in populations of individuals living with mental illness and personality disorders. Research on START has focused to some extent on assessments completed by research assistants. This study examined the implementation of START into a large forensic psychiatric service and reports on the psychometric properties of the measure when completed by multidisciplinary treatment teams. All START forms completed over a one-year period were evaluated ( N= 1057). Results indicate good structural reliability and excellent dispersion across the items, scales, and risk estimates. Few differences were noted by patient gender. Signature risk signs were much more common than expected (27%). It is notable that few patients were determined to be high risk on any of the seven risk estimates, with the exception of substance abuse. Overall, the results provide preliminary evidence for the success of the implementation and the value of the START for informing needs and treatment planning in forensic services.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".