Workplace Predictors of Violence against Nurses Using Machine Learning Techniques: A Cross-Sectional Study Utilizing the National Standard of Psychological Workplace Health and Safety
Bibliographic record
Abstract
BACKGROUND: Nurses experience an alarming rate of violence in the workplace. While previous work has indicated that working conditions play an important role in workplace violence outcomes, these studies have not used comprehensive and systematically operationalized variables. METHODS: Through cross-sectional survey responses from 4066 British Columbian nurses, we identified which of the 13 psychosocial factors, as outlined in the National Standard of Psychological Workplace Health and Safety, are most predictive of workplace violence perpetrated against nurses by patients and their visitors (Type II violence) and organizational employees (Type III violence). RESULTS: Eighty-seven percent of respondents indicated that they had experienced Type II violence, whereas 48% indicated they had experienced Type III violence over the last year. Lack of physical safety, workload management, and psychological protection were the top three psychosocial factors in the workplace predictive of Type II violence, whereas lack of civility and respect, organizational culture, and psychological support were the top three factors associated with Type III violence. CONCLUSIONS: The findings in this study shed light on the distinct psychosocial factors in the workplace in need of investment and intervention to address Type II and III violence.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".