Violence Prevention Climate in Civil and Forensic Mental Health Settings: Common Goal, Different Views?
Bibliographic record
Abstract
This study aimed to explore staff and patients’ views on the violence prevention climate in civil and forensic mental health settings. We conducted a cross-sectional survey of 110 inpatients and 198 staff members from three civil mental health hospitals (including two forensic units) and one forensic mental health hospital in Canada. Staff and patients’ perceptions of the violence prevention climate on civil and forensic mental health units were measured using the modified violence prevention climate scale, French version (VPC-M-FR). Multiple analyses of variance (ANOVAs) were performed to assess differences in the VPC-M-FR total and subscale scores (staff action, patient action, therapeutic environment) between patients and staff, settings (civil vs. forensic), restrictive practices (presence vs. absence of seclusion or restraints), incidents of violence during hospitalization (presence vs. absence), and victimization (presence vs. absence). In both settings, patients’ views of the violence prevention climate were significantly more positive than those of the staff. Staff in forensic mental health settings had a more positive perception of the violence prevention climate than those in civil mental health units. The results contribute to a better understanding of the violence prevention climate among staff and patients and will guide future interventions within civil and forensic settings.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".