Prevalence and Reporting of Needle Stick Injuries: A Survey of Surgery Team Members in Kermanshah University of Medical Sciences in 2012
Bibliographic record
Abstract
BACKGROUND: Surgeons are one of the groups, most highly exposed to the risk of needle stick injuries at work. The present study aims to determine the prevalence and reporting of needle stick injuries during the first 6 months of 2012, in faculty surgeons affiliated to the Kermanshah University of Medical Sciences. METHODS: In a cross-sectional descriptive-analytical survey, 29 surgeons were studied based on the census method. A reliable and valid questionnaire was used as a research instrument to collect the data. Data was analyzed using SPSS v.16 and based on descriptive and inferential statistics. RESULTS: Among 29 recruited surgeons, 5 (17.2%) had needle stick injuries during the 6 months, only one of whom had followed the established guidelines about reporting and following treatment. The most common instrument causing injury was the suture needle (60%). Significant differences were found in both groups of the injured and non-injured in term of gender (X(2)=5.612, P= 0.003), and number of patients (Z= 2.40, P=0.016) and daily working hours (Z=2.85, P=0.04). CONCLUSIONS: In relation to the relatively high prevalence of needle stick injuries among the surgeons and their lack of reporting, it is suggested that the Safety Guidelines in the operating room are carefully observed. Moreover, safer and lower risk surgical Instruments should be used.
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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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".