Nurses' Workplace Violence Reporting Behaviours and Reasons for Not Formally Reporting: A Cross‐Sectional Secondary Analysis
Bibliographic record
Abstract
AIM: To investigate predictors of nurses' reporting behaviours and their reasons for not formally reporting. BACKGROUND: Underreporting of workplace violence (WPV) among nurses contributes to gaps in WPV prevention measures, as it cannot be fully understood. WPV is classified according to its source (Type II: patients and visitors, Type III: coworkers) and forms (physical assault, threat of assault, emotional abuse, verbal sexual harassment and sexual assault). DESIGN: This is a secondary analysis of cross-sectional survey data collected in 2019 from British Columbia (BC), Canada. METHODS: This study had a sample of 4109 BC nurses. Multinomial logistic regression was used to analyse predictors of reporting behaviours. Reasons for not reporting were analysed descriptively. RESULTS: Informal reporting to management or through a patient safety incident report was less likely when nurses experienced threat of assault, emotional abuse and verbal sexual harassment from both Type II and III sources and physical assault from Type III sources. Higher perceptions of WPV prevention efforts increased odds of informal and formal reporting through employee incident procedures. Believing that nothing would change after reporting remained among the top three reasons for not formally reporting across all WPV sources and forms. Nurses also commonly selected not knowing the formal process, lack of leadership support and other reasons stated in an open-text response. CONCLUSION: Findings indicate that nurses in BC, Canada, perceive many barriers to formal WPV reporting. Formal reporting systems should address these barriers so that healthcare organisations can effectively track WPV and have data to inform WPV prevention measures. IMPLICATIONS: To promote WPV reporting, healthcare organisations need multifaceted interventions including confidential and simplified reporting systems, leadership support to follow-up with nurses and education and training on reporting systems. REPORTING METHOD: The authors of this manuscript have adhered to the relevant EQUATOR guidelines based on the STROBE cross-sectional reporting method. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".