Implementing the Dynamic Appraisal of Situational Aggression in Mental Health Units
Bibliographic record
Abstract
PURPOSE: The aims of this study are to explain the intervention of implementing a structured violence risk assessment procedure in mental health inpatient units using the Ottawa Model of Research Use (OMRU) as a guiding framework and to consider nurses' perspectives of its clinical utility and implementation process. BACKGROUND: Patient aggression toward staff is a global concern in mental health units. The limited extant literature exploring the use of structured violence risk assessments in mental health units, although small and inconsistent, reveals some positive impacts on the incidence of aggression and staff's use of restrictive interventions. RATIONALE: Although numerous violence risk assessment instruments have been developed and tested, their systematic implementation and use are still limited. DESCRIPTION OF THE PROJECT: A project titled "Safer Working Management" (111298) was conducted in a Finnish hospital district, across 3 mental health units. The 6 steps of OMRU were followed during implementation of the Dynamic Appraisal of Situational Aggression (DASA). OUTCOME: Nurses' views toward structured violence risk assessment procedures varied. Although implementation of the DASA was seen as a useful method to increase discussions with patients and nursing staff, some staff preferred their own clinical judgment for assessment of violence risk. CONCLUSION: It is possible to use a specific model to promote the implementation of risk assessment instruments in mental health units. However, the complex mental health inpatient environment and the difficulties in understanding and managing aggressive patients present challenges for the implementation of structured violence risk assessment methods. IMPLICATIONS: The OMRU provides a tool for clinical nurse specialists to guide implementation process in mental health units. Clinical nurse specialists must promote training for staff regarding use of new innovations, such as the DASA. Implementation processes should be reviewed so that clinical nurse specialists can lead and support mental health staff to properly use structured violence risk assessment measures.
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".