Improving Knowledge of Needle-Stick Injury Prevention: A Two-Cycle Clinical Audit From Sudan
Bibliographic record
Abstract
Background Needle-stick injuries (NSIs) remain a major occupational hazard for healthcare workers, exposing them to blood-borne infections. Globally recognized occupational health standards emphasize structured training, safe sharps handling practices, and timely access to post-exposure prophylaxis (PEP). This audit evaluated staff knowledge and awareness of NSI prevention and management in a Sudanese teaching hospital, aiming to identify gaps and assess the impact of targeted interventions. Methods A two-cycle clinical audit was conducted over twelve months (September 2024-August 2025) at Bashair University Hospital. A structured, self-administered questionnaire was distributed to doctors, nurses, and laboratory technicians in both cycles (n = 90 per cycle). The tool assessed knowledge of NSI risk pathogens, immediate first-aid response, PEP initiation, sharps disposal practices, vaccination awareness, and reporting procedures. Interventions included structured teaching sessions, posters, departmental reinforcement, and distribution of guideline summaries. Data were analyzed descriptively and compared between cycles. Results Knowledge of HBV as the most common pathogen improved substantially among doctors (15.8%→81.2%), nurses (15.8%→100%), and technicians (65%→100%). Awareness of immediate wound washing also improved, reaching 100% among nurses and technicians. Understanding of PEP as medication increased markedly (doctors: 52.6%→93.8%; nurses: 52.6%→100%; technicians: 25%→100%). However, awareness of formal reporting systems declined sharply among doctors (89.5%→12.5%) and technicians (50%→5%). Participation in refresher training fell across groups, while perceived training adequacy showed only partial improvement. Conclusion Targeted interventions improved healthcare workers' knowledge of NSI prevention and management; however, persistent gaps in training sustainability and reporting culture indicate that these gains may not be maintained without continued institutional support. Sustained improvement requires structured refresher programs, robust reporting systems, and administrative commitment to embedding sharps safety into routine practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".