Evaluation of an Education and Training Program to Prevent and Manage Patients’ Violence in a Mental Health Setting: A Pretest-Posttest Intervention Study
Bibliographic record
Abstract
Workplace violence can lead to serious consequences for victims, organizations, and society. Most workplace violence prevention programs aim to train staff to better recognize and safely manage at-risk situations. The Omega education and training program was developed in Canada in 1999, and has since been used to teach healthcare and mental health workers the skills needed to effectively intervene in situations of aggression. The present study was designed to assess the impact of Omega on employee psychological distress, confidence in coping, and perceived exposure to violence. This program was offered to 105 employees in a psychiatric hospital in Montreal, Canada. Eighty-nine of them accepted to participate. Questionnaires were completed before the training, after a short period of time (M = 109 days) and at follow-up (M = 441 days). Repeated-measures ANOVAs and Cohen's d effect sizes were calculated. Results demonstrated statistically significant improvements in short-term and follow-up posttest scores of psychological distress, confidence in coping, and in levels of exposure to violence. This study is one of very few to demonstrate the positive impact of this training program. Further research is needed to understand how to improve the effectiveness of the program, especially among participants resistant to change.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".