Bibliographic record
Abstract
Patients in forensic mental health care have a difficult journey through inpatient rehabilitation and re-integration into the community. Risk assessment guides this progress, usually with clinician-based processes that use structured risk-assessment tools. Patients' understanding of their own risk is important to inform risk assessment and the chances of successful rehabilitation. The emergence of shared decision-making approaches provides an opportunity to consider shared risk assessment and formulation. We reviewed the literature to explore models of patients' involvement in risk assessment and the impact on outcomes in forensic mental health care. We conducted searches of three databases (Medline, PsychINFO, and EMBASE) to identify papers that employed shared risk understanding for violence risk. Additional records were identified through review of citations, with articles being selected using a predetermined set of inclusion and exclusion criteria. We found five studies that met the inclusion criteria for patient involvement in risk assessment with measurement of construct or predictive validity. The studies employed diverse methodologies, but they suggest that patient involvement in assessing risk is feasible when correlated with staff ratings. There is encouraging evidence of the predictive validity of self-rated risk alongside staff-rated risk assessment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".