Understanding and measuring workplace violence in healthcare: a Canadian systematic framework to address a global healthcare phenomenon
Bibliographic record
Abstract
Abstract Background Globally, healthcare institutions have seen a marked rise in workplace violence (WPV), especially since the Covid-19 pandemic began, affecting primarily acute care and emergency departments (EDs). At the University Health Network (UHN) in Toronto, Canada, WPV incidents in EDs jumped 169% from 0.43 to 1.15 events per 1000 visits (p < 0.0001). In response, UHN launched a comprehensive, systems-based quality improvement (QI) project to ameliorate WPV. This study details the development of the project’s design and key takeaways, with a focus on presenting trauma-informed strategies for addressing WPV in healthcare through the lens of health systems innovation. Methods Our multi-intervention QI initiative was guided by the Systems Engineering Initiative for Patient Safety (SEIPS) 3.0 framework. We utilized the SEIPS 101 tools to aid in crafting each QI intervention. Results Using the SEIPS 3.0 framework and SEIPS 101 tools, we gained a comprehensive understanding of organizational processes, patient experiences, and the needs of HCPs and patient-facing staff at UHN. This information allowed us to identify areas for improvement and develop a large-scale QI initiative comprising 12 distinct subprojects to address WPV at UHN. Conclusions Our QI team successfully developed a comprehensive QI project tailored to our organization’s needs. To support healthcare institutions in addressing WPV, we created a 12-step framework designed to assist in developing a systemic QI approach tailored to their unique requirements. This framework offers actionable strategies for addressing WPV in healthcare settings, derived from the successes and challenges encountered during our QI project. By applying a systems-based approach that incorporates trauma-informed strategies and fosters a culture of mutual respect, institutions can develop strategies to minimize WPV and promote a safer work environment for patients, families, staff, and HCPs.
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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 | 0.183 | 0.204 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.045 | 0.040 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".