A Scoping Review of Patient Involvement in Violence Risk Assessment
Bibliographic record
Abstract
OBJECTIVE: This scoping review aimed to summarize the published literature on patient involvement in violence risk assessment. Two research questions reviewed the extent of patient involvement and what evidence exists. INCLUSION CRITERIA: English-language peer-reviewed published articles of any methodology related to violence risk assessment toward others were included. Articles were related to forensic and mental health practice and involve patients directly in the process. METHODS: Five electronic databases were comprehensively searched, as well as the reference lists of included articles. Both authors reviewed articles for inclusion and extracted data from included articles. RESULTS: Fifteen articles met the inclusion criteria. Articles reported on three approaches to patient engagement in structured violence risk assessment: how patients were involved or experienced the process, using rating scales, and using questions related to patient self-perceived risk. In relation to what evidence existed, four main themes emerged: patient views about risk and their involvement in risk assessment, comparing the predictive accuracy of patient self-rated tools with clinician-rated tools, predictive accuracy of a patient self-rated tool, and comparing risk ratings between patients and clinicians. CONCLUSIONS: There is a dearth of research published about involving patients in their own risk assessment. Patients report both positive and negative experiences of the process. From cohort-type studies, results have shown that patient self-risk assessment can have a similar predictive ability to the clinician ratings related to adverse violence outcomes. Findings from studies can pave the way for future clinical research around the tools that have been developed thus far.
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 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 | 0.027 | 0.112 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.028 | 0.027 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".