Prevalence of Needlestick Injuries, Attitude Changes, and Prevention Practices Over 12 Years in an Urban Academic Hospital Surgery Department
Bibliographic record
Abstract
OBJECTIVE: Needlestick injury prevalence, protection practices, and attitudes were assessed. Current medical students were compared with 2003 data to assess any changes that occurred with engineered safety feature implementation. BACKGROUND: Risk of occupational exposure to bloodborne pathogens is elevated in the operating room particularly with surgeons in training and nurses. METHODS: A cross-sectional survey was distributed to medical students (n = 358) and Department of Surgery staff (n = 247). RESULTS: The survey response rate was 24.8%. Needlestick injuries were reported by 38.7% of respondents (11% high risk), and the most common cause was "careless/accidental." Needlestick injury prevalence increased from medical students to residents and fellows (100%). Thirty-three percent of injured personnel had at least one unreported injury, and the most common reason was "inconvenient/too time consuming." Needlestick injury prevalence and double-glove use in medical students did not differ from 2003, and 25% of fellows reported always wearing double gloves. The true seroconversion rate for bloodborne pathogens was underestimated or unknown. The concern for contracting a bloodborne pathogen significantly decreased (65%) compared to 2003, and there were significantly less medical students with hepatitis B vaccinations (78.3%). Level of concern for contracting a bloodborne pathogen was predictive of needlestick injury. CONCLUSIONS: Needlestick injury and occupational exposure to bloodborne pathogens are significant hazards for surgeons and nurses. Attitudes regarding risk are changing, and the true seroconversion risk is underestimated. Educational efforts focused on needlestick injury prevalence, seroconversion rates, and double-glove perforation rates may be effective in implementing protective strategies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".