Workplace Violence among British Columbia Nurses Across Different Roles and Contexts
Bibliographic record
Abstract
Workplace violence in healthcare settings is on the rise, particularly against nurses. Most healthcare violence research is in acute care settings. The purpose of this paper is to present descriptive findings on the prevalence of types and sources of workplace violence among nurses in different roles (i.e., direct care, leader, educator), specialties, care sectors (i.e., acute, community, long-term care) and geographic contexts (i.e., urban, suburban, rural) within the province of British Columbia (BC), Canada. This is a province-wide survey study using a cross-sectional descriptive, correlational design. An electronic survey was emailed by the provincial union to members across the province in Fall 2019. A total of 4462 responses were analyzed using descriptive and chi-square statistics. The most common types of workplace violence were emotional abuse, threats of assault and physical assault for all nursing roles and contexts. Findings were similar to previous BC research from two decades ago except for two to ten times higher proportions of all types of violence, including verbal and physical sexual assault. Patients were the most common source of violence towards nurses. Nurses should be involved in developing workplace violence interventions that are tailored to work environment contexts and populations.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".