Needle Stick Injuries – Risk and Preventive Factors: A Study among Health Care Workers in Tertiary Care Hospitals in Pakistan
Bibliographic record
Abstract
BACKGROUND: Health care workers (HCWs) are at substantial risk of acquiring blood borne infections such as HIV, Hepatitis-B and Hepatitis-C through needle stick injuries (NSIs). This study aimed to assess the proportion of NSIs and their associated factors among HCWs and also to identify the areas in which preventive efforts might be directed to protect against this occupational hazard. METHODOLOGY: A cross-sectional study was conducted in two tertiary care hospitals of Pakistan representing both private and public health sector. A total of 497 HCWs (doctors and nurses) were interviewed using a structured questionnaire. Data was collected from January to May 2008. RESULTS: Overall, 64% of the HCWs were exposed to at least one NSI during their career; among them 73% reported NSIs for two or more times. Factors found to be highly associated with NSIs were those practicing this occupation for more than five years (p < 0.001: OR = 5.92; 95% CI = 3.45-10.16) and working as nurse than doctor (p 0.001: OR = 2.12; 95% CI = 1.35-3.32). Having received booster dose of hepatitis B vaccine (p 0.02: OR = 1.85; 95% CI = 1.10-3.11), working in surgical specialty (p < 0.01: OR = 1.6; 95% CI = 1.09-2.51) and being a female (p 0.03: OR = 1.52; 95% CI = 1.04-2.22) were also found to be associated with NSIs. Most commonly reported reason for NSIs was injecting medicine and drawing blood (42%) followed by two-handed recapping of needle (37%). Only, 34% of study subjects were vaccinated against hepatitis B infection. Overall, HCWs had inadequate practices regarding standard precautions such as availability of gloves/protective cloths (40%) and infection control guidelines/protocols (10%) respectively in their working places. CONCLUSION: In addition to very high rates of NSIs, low safety practices including inadequate vaccination coverage, unavailability of infection control guidelines and other preventive facilities were reported in this study. Prevention of occupational infections among HCWs should be a priority. Formal training, by health authorities in the local area, about safe practices and availability of preventive facilities should be ensured regarding NSIs among HCWs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| 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 | 0.001 | 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".