Systematic Violence Monitoring to Reduce Underreporting and to Better Inform Workplace Violence Prevention Among Health Care Workers: Before-and-After Prospective Study
Bibliographic record
Abstract
BACKGROUND: Monitoring workplace violence (WPV) against health care workers (HCWs) through incident reporting is crucial to drive prevention, but the actual implementation is spotty and experiences underreporting. OBJECTIVE: This study aims to introduce a systematic WPV surveillance in 2 public referral hospitals in Italy and assess underreporting, WPV annual rates, and attributes "before" (2016-2020) and "after" its implementation (November 2021 to 2022). METHODS: During 2016-2020, incident reporting was based on procedures and data collection forms that were neither standardized between hospitals nor specific for aggressions. We planned and implemented a standardized WPV surveillance based on (1) an incident report form for immediate and systematic event notification, adopting international standards for violence definitions; (2) second-level root cause analysis with a dedicated psychologist, assessing violence determinants and impacts and offering psychological counseling; (3) a web-based platform for centralized data collection; and (4) periodic training for workforce coordinators and newly hired workers. We used data from incident reports to estimate underreporting, defined as an observed-to-expected (from literature and the "before" period) WPV ratio less than 1, and the 12-month WPV rates (per 100 HCWs) in the "before" and "after" periods. During the latter period, we separately estimated WPV rates for first and recurrent events. RESULTS: In the "before" period, the yearly observed-to-expected ratios were consistently below 1 and as low as 0.27, suggesting substantial violence underreporting of up to 73%. WPV annual rates declined in 1 hospital (from 1.92 in 2016 to 0.57 in 2020) and rose in the other (from 0.52 to 1.0), with the divergence being attributable to trends in underreporting. Available data were poorly informative to identify at-risk HCW subgroups. In the "after" period, the observed-to-expected ratio rose to 1.14 compared to literature and 1.91 compared to the "before" period, consistently in both hospitals. The 12-month WPV rate was 2.08 (95% CI 1.79-2.42; 1.52 and 2.35 in the 2 hospitals); one-fifth (0.41/2.08, 19.7%) was due to recurrences. Among HCWs, the youngest group (3.79; P<.001), nurses (3.19; P<.001), and male HCWs (2.62; P=.008) reported the highest rates. Emergency departments and psychiatric wards were the 2 areas at increased risk. Physical assaults were more likely in male than female HWCs (45/67, 67.2% vs 62/130, 47.7%; P=.01), but the latter experienced more mental health consequences (46/130, 35.4% vs 13/67, 19.4%; P=.02). Overall, 40.8% (53/130) of female HWCs recognized sociocultural (eg, linguistic or cultural) barriers as contributing factors for the aggression, and 30.8% (40/130) of WPV against female HCWs involved visitors as perpetrators. CONCLUSIONS: A systematic WPV surveillance reduced underreporting. The identification of high-risk workers and characterization of violence patterns and attributes can better inform priorities and contents of preventive policies. Our evaluation provides useful information for the large-scale implementation of standardized WPV-monitoring programs.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".