A quality improvement project to reduce intravenous catheter related infections in the neonatology unit of Kibogora hospital in Rwanda
Bibliographic record
Abstract
In developing countries, intravenous (IV) catheter related infections (CRI) rate is generally high. Neonates are more susceptible to develop CRI. We examined the impact of a quality improvement project on IV CRI rates in the neonatal intensive care unit (NICU) of a district hospital in Rwanda. A pre- and post-intervention study was conducted from 2014 to 2016 to evaluate the IV CRI rate and nurses’ IV management technique. A written test was administered to evaluate their knowledge on the matter. The intervention had three components: First implementing an IV management policy. Secondly, training staff on the policy and finally, managers provided support and supervision during the change. We measured five indicators: (1) the IV CRI rate; (2) the percentage of nurses who tested 80% on IV management knowledge; (3) the percentage of IV devices changed following the World Health Organization (WHO) guideline; (4) IV management technique; and (5) the hospital length of stay (LOS). The IV CRI rate reduced from 32.1% to 14.5% (p < .001). The hospital LOS reduced from 15.31 to 7.43 days (p < .001). The compliance of changing IV following WHO guideline increased from 0% to 99% (p < .001); proper IV management technique use increased from 43% to 96% (p < .001); the mean rank of staff on IV management knowledge score significantly increased from 3.5 to 9.5 (p = .004). This project demonstrates that a quality improvement project can help address the IV CRI at very low cost in a resource-challenged setting.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".