Implementation of risk assessment tools in psychiatric services
Bibliographic record
Abstract
Violence remains a major risk management concern in psychiatric services with implications on the safety and well-being of patients, staff, and the public. Serious physical and psychological consequences of violence involving property damage, bodily injuries, and threat to life have been reported in mental health services. Risk assessment tools are important safeguard measures; however, research on clinical implementation is presently limited. Structured professional judgment (SPJ) risk management tools that incorporate professional discretion with analytical understanding of evidence-based risk factors are widely accepted for risk assessment. However, clinical utility is suboptimal due to several barriers, including those related to the tool, the clinical setting, and resistance from health professionals. To better understand the challenges militating against optimal implementation of risk assessment tools, we reviewed and presented some lessons from the implementation of clinical practice guidelines on a general scale and our experience implementing an SPJ tool called Hamilton Anatomy of Risk Management across a variety of psychiatric services. In summary, the clinical utility of risk assessment tools improves if the tool is psychometrically sound, concise, consensus rated, time efficient, and practical for planning risk management. User feedbacks on the tool utility are also important to sustain implementation.
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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 | 0.171 | 0.412 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".