Validating the SAPROF with Forensic Mental Health Patients
Bibliographic record
Abstract
Assessing and managing level of violence risk among forensic mental health patients is a primary role of clinical staff. Forensic risk assessments are typically focused on risk factors and deficits, whereas patient protective factors are either partially included or excluded by clinicians. The Structured Assessment of Protective Factors for Violence Risk (SAPROF) is a recently developed measure of protective factors designed to augment the conventional use of risk assessment tools with a correctional/forensic population. In the current study, the psychometric properties of the SAPROF were examined in a sample of 50 forensic inpatients and outpatients found Not Criminally Responsible (NCR) at a psychiatric hospital in Ontario, Canada. The SAPROF was found to have adequate internal, intrarater, and interrater reliability. Using a subsample of inpatients only, incremental predictive validity was demonstrated for institutional misconducts at six-month follow-up over the HCR-20 V3. Taken together, the results suggest that the SAPROF may be a useful addition to clinical practice and inform review board decisions about risk prediction, risk management, and treatment planning.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".