Evaluation of a Luer-Activated Intravenous Administration System
Bibliographic record
Abstract
Abstract Needlestick and other sharps-related injuries are largely preventable with proper education, training, and the use of safety-engineered devices. In 2009, a review of clinical practice was completed at Thunder Bay Regional Health Sciences Centre. The review revealed that despite needle-free legislation, numerous years of education on the dangers of using needles, and the availability and importance of using safety devices, nurses and physicians continued to use needles when accessing intravenous tubing to administer medication. During 2010, a luer-activated intravenous administration system was introduced to replace the current split-septum intravenous administration system. Implementation of the luer-activated system was expected to decrease needlestick injuries, positively affect nursing practice, and demonstrate a commitment to a safe working environment. Reported needlestick injuries were reviewed and analysed pre- and post-implementation and a survey on nurse perception of the new system and organizational safety was distributed. Results showed that there was a 46% decrease in needlestick injuries post-implementation, along with 80% of nursing staff reporting that the new system had a positive influence on their nursing practice and belief that the organization was committed to providing a safe work environment. The results of this study emphasize and support the replacement of needles with alternative needleless products to improve the safety of the work environment.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".