The reliability and validity of the SAPROF among forensic mental health patients
Bibliographic record
Abstract
Assessing and managing level of risk among forensic mental health patients is a primary role of clinical forensic psychologists. Forensic assessments are focused on risk factors and deficits, whereas patient strengths and protective factors are either partially included or overlooked altogether by forensic psychologists. As a result, less is known about protective factors in general and how they may serve to inform risk management practices. The Structured Assessment of Protective Factors for Violence Risk (SAPROF) is the first tool to exclusively rely on protective factors and was investigated for the current study. The psychometric properties of the SAPROF were examined using a sample of 50 Canadian patients found Not Criminally Responsible (NCR) at a psychiatric hospital using both file information and semi-structured interviews. Outcome variables included risk management decisions (change in privilege level and security level) and indicators of recidivism (psychiatric medication administration, institutional misconduct and disposition breaches). The study found some evidence for intrarater and interrater reliability, construct validity, predictive validity and incremental predictive validity. The SAPROF approached significance for adding incremental predictive validity to the HCR-20 V3, a measure of violence risk, for disposition breaches and institutional misconduct, and effect sizes doubled. Given that the addition of the SAPROF increased the accuracy of the violence risk assessment, there are considerable implications for informing clinical practice. Implications for risk assessment, treatment planning, intervention and risk management decisions implemented by review boards and clinical practitioners are discussed. It is recommended that the SAPROF be added as an adjunct measure to risk assessment batteries and included in hospital reports, given that it predicted several patient behaviours.
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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 | 0.017 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".