Needle stick injuries in healthcare workers of a secondary Care Hospital, Pakistan.
Bibliographic record
Abstract
Needle Stick Injury (NSI) is a percutaneous piercing wound typically dealing with sharps. Needle stick injuries are the most common health care workers issue worldwide. The causes include various factors like type and design of needle, recapping activity, handling/transferring specimens, collision between HCWs or sharps, during clean-up, manipulating needles in patient line related work, passing/handling devices or failure to dispose of the needle in puncture proof containers. NSIs may transmit other bacterial, fungal, or viral infections, including blastomycosis, cryptococcosis, diphtheria, herpes, malaria, mycobacteriosis, spotted fever and syphilis. Objectives: To determine frequency of needle stick injury among health care workers. Study Design: Cross-sectional study. Setting: District Headquarter Hospital Layyah. Period: Jan to March 2019. Material & Methods: Sample size was 161. A structured pre-tested questionnaire containing both open and close-ended questions was administered during the period of Jan-March 2019. Results: Out of 161 participants, 114 (70.8%) reported having a needle stick injury at least once during their clinical practice and the frequency of NSIs was significantly higher among nurses (76.7%) as compared to Doctors (50%), Laboratory staff (45.5%) and waste handlers (70.8 %). Conclusion: Study concludes that in absence of the routine collection of accurate data on NSIs, small studies have been useful in highlighting which groups of HCWs are most at risk from NSIs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".