Health Care Staff–Reported Workplace Violence in Patient Safety Event Reports
Bibliographic record
Abstract
Importance: Workplace violence (WPV) against health care staff is an important problem in the US and worldwide. Objective: To develop a WPV classification approach and investigate the characteristics of the WPV incidents committed against health care staff documented in patient safety event (PSE) reports. Design, Setting, and Participants: This cross-sectional study analyzed 975 self-reported PSEs recorded by health care staff from March 1 to September 20, 2023, from a multihospital health care system in the mid-Atlantic region of the US. Main Outcomes and Measures: A subset of PSE reports that potentially pertained to WPV incidents were identified through structured information of the reports. When available, the following information was captured from each report: type of WPV incident and harm, reported perpetrators and those exposed to WPV, precipitating factors to the WPV incident, reporter's job function, whether security officers or law enforcement were contacted, and facility type. Results: A total of 15 426 PSEs were recorded. Of those, 975 reports (6.3%) were selected; for 300 (30.8%) of these reports, the free-text description of incidents was reviewed by 2 independent investigators, interrater reliability (IRR) was calculated, and the WPV classification was developed. Two investigators then independently classified the remaining 675 reports. A median IRR of 84% (IQR, 68%-99%) was achieved. Eight hundred thirty-one reports (85.2%) were related to WPV and 144 (14.8%) did not contain WPV narratives. The 831 WPV reports were further analyzed, and additional information about the WPV incidents were identified: patient- or visitor-on-staff violence (673 [76.7%]), verbal harm (331 [39.8%]), patients as perpetrators (581 [69.9%]), nurses exposed to WPV (277 [33.3%]), agitation (193 [23.2%]) and aggression (179 [21.5%]) as the leading precipitating factors, nurses reporting incidents (533 [64.1%]), contact of security officers (391 [47.1%]) and law enforcement (70 [8.4%]), and incidents occurring in hospitals (767 [92.3%]). Conclusions and Relevance: In this cross-sectional study of 15 426 PSE reports, 831 WPV incidents were identified, most involving patient- or visitor-on-staff verbal harm, with nurses frequently exposed. Agitation and aggression were the leading precipitating factors. The insights from PSE reports can inform the development of targeted WPV intervention and prevention plans, ultimately enhancing the safety of frontline staff.
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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 | 0.015 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".