Preventing aggression in psychiatric settings: a best practice implementation project
Bibliographic record
Abstract
INTRODUCTION: Aggression is a frequent occurrence in psychiatric settings and results from complex multifactorial phenomena. Verbal or physical aggression has a significant impact on the quality of care, with negative repercussions on patients, professionals, and health care institutions. OBJECTIVES: This project aimed to prevent and manage hetero-aggression in a university hospital psychiatric department in Switzerland through the promotion of evidence-based practices. METHODS: The project used JBI's Evidence Implementation Framework, which is grounded in an audit and feedback process. A baseline audit was conducted to measure current practices for preventing and managing aggression and compare these to eight best practice recommendations. Interventions to improve compliance with best practices were implemented, and a follow-up audit was conducted to measure the changes achieved. RESULTS: Despite a high prevalence of staff exposure to aggression, the baseline audit showed that violence risk assessments were not systematically documented. The follow-up audit revealed improvements, with the use of a validated screening tool to identify violence risk and increased prevention interventions. However, these measures had a relatively low impact on the exposure to violence of health care professionals. Patient involvement in the violence risk assessment also remained low. CONCLUSIONS: The JBI approach used in this project led to significant improvements in professional practices related to violence risk assessment and reduced the gaps between recommendations and clinical practices. Clinical practice analysis sessions are a successful means of promoting understanding of prevention interventions. SPANISH ABSTRACT: http://links.lww.com/IJEBH/A410.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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".