Violence Prevention Climate Scale: Translation, Adaptation, and Psychometric Assessment of the French Canadian Version
Bibliographic record
Abstract
Introduction: Violence in psychiatric settings has negative consequences on patients, staff, and the institution alike. Efforts to prevent violence cannot currently be assessed due to a lack of suitable indicators. The Violence Prevention Climate Scale (VPC-14) is a validated tool that can be filled out by both staff and patients to assess the violence prevention climate in mental health care units. Objective: This study aimed to conduct the translation and adaptation of the VPC-14 to a French Canadian context, and to assess its psychometric properties in general and forensic psychiatric settings. Methods: This study followed a transcultural approach for validating measuring instruments. Psychometric properties were assessed in 308 patients and staff from 4 mental health and forensic hospitals in Quebec (Canada). Content validity was assessed using a bilingual participant approach. Internal validity was examined through exploratory factor analysis and internal consistency for each care setting using Cronbach’s alpha coefficient analysis. Results: The Échelle modifiée du climat de prévention de la violence [Modified Violence Prevention Climate Scale] (VPC-M-FR) consists of 23 items with a 3-factor structure: 1) staff action, 2) patient action, and 3) the therapeutic environment. Cronbach’s alphas ranging from 0.69 to 0.89 were obtained for the internal consistency of the scale. Discussion and conclusion: The VPC-M-FR has satisfactory psychometric properties for measuring the violence prevention climate in mental health and forensic settings. By measuring the violence prevention climate from the standpoint of patients and staff, targeted preventive measures can be implemented to improve safety for all.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".