Prevalence of needle-stick injury among nursing students: A systematic review and meta-analysis
Bibliographic record
Abstract
Introduction Needle-stick injuries (NSI) are a serious threat to the health of healthcare workers, nurses, and nursing students, as they can expose them to infectious diseases. Different prevalence rates have been reported for this type of injury in different studies worldwide. Therefore, this study aimedto estimate the pooled prevalence of NSI among nursing students. Methods This study was conducted by searching for articles in Web of Science, PubMed, Scopus, Embase, and Google Scholar without time limitation using the following keywords: needle-stick, needle stick, sharp injury, and nursing student. The data were analyzed using the meta-analysis method and random-effects model. The quality of the articles was evaluated with Newcastle-Ottawa Quality Assessment Scale (NOS). The heterogeneity of the studies was examined using the I 2 index, and the collected data were analyzed using the STATA Software Version 16. Results Initially, 1,134 articles were retrieved, of which 32 qualified articles were included in the analysis. Nursing students reported 35% of NSI (95% CI: 28–43%) and 63% (95% CI: 51–74%) did not report their needle-stick injuries. The highest prevalence was related to studies conducted in Asia (39.7%; 95% CI: 31.7–47.7%). There was no significant correlation among NSI prevalence and age of samples, and article year of publication. Conclusion A third of nursing students reported experiencing NSI. Consequently, occupational hazard prevention training and student support measures need to be considered.
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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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.042 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".