Sharp-Device Injuries to Hospital Staff Nurses in 4 Countries
Bibliographic record
Abstract
OBJECTIVE: To compare sharp-device injury rates among hospital staff nurses in 4 Western countries. DESIGN: Cross-sectional survey. SETTING: Acute-care hospital nurses in the United States (Pennsylvania), Canada (Alberta, British Columbia, and Ontario), the United Kingdom (England and Scotland), and Germany. PARTICIPANTS: A total of 34,318 acute-care hospital staff nurses in 1998-1999. RESULTS: Survey-based rates of retrospectively-reported needlestick injuries in the previous year for medical-surgical unit nurses ranged from 146 injuries per 1,000 full-time equivalent positions (FTEs) in the US sample to 488 injuries per 1,000 FTEs in Germany. In the United States and Canada, very high rates of sharp-device injury among nurses working in the operating room and/or perioperative care were observed (255 and 569 injuries per 1,000 FTEs per year, respectively). Reported use of safety-engineered sharp devices was considerably lower in Germany and Canada than it was in the United States. Some variation in injury rates was seen across nursing specialties among North American nurses, mostly in line with the frequency of risky procedures in the nurses' work. CONCLUSIONS: Studies conducted in the United States over the past 15 years suggest that the rates of sharp-device injuries to front-line nurses have fallen over the past decade, probably at least in part because of increased awareness and adoption of safer technologies, suggesting that regulatory strategies have improved nurse safety. The much higher injury rate in Germany may be due to slow adoption of safety devices. Wider diffusion of safer technologies, as well as introduction and stronger enforcement of occupational safety and health regulations, are likely to decrease sharp-device injury rates in various countries even further.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".