Needlestick Injuries in Healthcare, Research and Veterinary Environments in a Sample Population in British Columbia and their Economic, Psychological and Workplace Impacts
Bibliographic record
Abstract
Abstract Introduction Needle-stick injuries (NSIs) are defined as the sharp point of a needle puncturing human skin. This article examines the risk and illustrates the burden of NSIs for workers in the healthcare, veterinary and research industries, and includes a sample survey population of workers in workplaces using needles. Methods For the review component of this article, PubMed and Google Scholar were queried within the date range of 1998-2022, retrieving 1,437 results. A publicly available sample population dataset was and analyzed from British Columbia (n=30) on workplace needlestick injuries. The OSHA, WHO, and NIEHS guidelines were reviewed, and the WorkSafe BC injury database was searched using FIPPA requests. Discussion Recapping remains a common practice despite decades of guidelines recommending against recapping. NSI research is underpowered and underrepresented in non-healthcare settings. NSIs lead to heightened anxiety, depression, and PTSD in workers and exposure to pathogens, toxic chemicals and permanent tissue damage. NSI annual reporting is likely an underestimate due to chronic underreporting, and the financial impact including work-loss and healthcare costs continues to rise. Current NSI prevention devices have limited uptake and thus, more affordable, versatile and efficient NSI-prevention devices are needed. Relevance Due to COVID-19, healthcare workers are at a higher risk of receiving NSIs. Emphasis on safe needle handling practices is necessary to maintain workers physical and psychological safety, to protect workers using COVID-19 PPE on long shifts, and to deliver the high volume of vaccinations required to inoculate the global population. Conclusion NSIs are detrimental to healthcare workers wellbeing, chronically underreported, and poorly surveyed. Areas of future research include determining more effective solutions to reduce NSIs, assessing the validity of NSI reporting systems, and integrating solutions with COVID-19 prevention and vaccination protocols.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".