Overview of Occupational Injuries Among Registered Nurses in Washington State, 2007 to 2019
Bibliographic record
Abstract
Background Registered nurses (RNs) represent the largest segment of the health care workforce and have unique job demands and occupational health considerations. The purpose of this study was to describe the incidence, cost, and causes of occupational injuries among RNs in Washington State and to quantify the cumulative cost and burden of each type of injury, relative to all injuries among RNs. Methods Annual injury claims data covered under Washington State workers’ compensation (WC) fund were analyzed over a 13-year period (2007–2019). Annual mean incidence and cost of injuries were calculated and stratified by nature, source, and event/exposure. Negative binomial regression models were used to examine trends in injury incidence over time, for injury incidence overall, and by the most common injury classifications. Results Between 2007 and 2019, 10,839 WC claims were filed and accepted for Washington State RNs (annual M = 834), totaling more than US$65 million. No significant trend in overall injury incidence was observed (incidence rate ratio [IRR]: 0.99, 95% confidence interval [CI] = [0.94, 1.05]). The most common injury exposures were bodily reaction and exertion, contact with objects and equipment, falls, and assaults and violent acts. Discussion To our knowledge, this is the first broad study of the incidence and costs of occupational injuries among RNs across all workplace settings. We identified high-cost, high-frequency incidence rates of musculoskeletal, sharp, and violence-related occupational injury claims, highlighting intervention targets. Implications for Occupational Health Practice: Policy makers, health systems, and occupational health nurse leaders can use this information to identify priority areas where evidence-based occupational health and prevention programs are most needed.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".