Bibliographic record
Abstract
Highlights Collaboration with industry is an effective model for large scale performance improvement. PIVC changes resulted in better first-stick success and PIVC dwell time. Staff training is a critical for sustaining performance improvement success. PIVC catheters with integrated extension tubing can reduce staff blood exposure. Abstract Background: Insertion of peripheral vascular access devices (PIVC) is fundamental to patient care and may affect patient outcomes. Baseline data of PIVC insertions at a large medical center revealed that catheters required multiple insertion attempts, catheter hubs were manipulated to place extension sets, increasing the risk of complications, dwell times did not meet current standards, nurses experienced blood-exposure risk, and overall compliance with the hospital documentation policy was suboptimal. A 3-phase quality improvement project was conducted to address these concerns. Methods: In Phase 1, an assessment of the current state of PIVC insertions and care was conducted using a mixed-methods approach consisting of an observational audit of insertion and maintenance practices, and retrospective chart reviews. In Phase 2, PIVC policies and practices were updated to reflect current standards. A new advanced design PIVC device was adopted, and education was provided to all staff. In Phase 3, the impact of these changes on key PIVC measures was assessed 1 year later. Results: The analysis of the data found several improvements following implementation of an integrated IV catheter system: first-stick success rate increased from 73% to 84%, staff blood exposure was reduced from 46.67% to 0% (P = .01), improper securement of PIVC catheters was reduced from 11% to 0% (P = .002), and documentation compliance rate increased from 68% to 80%. The median PIVC dwell time doubled (from 2 days to 4 days). Conclusion: Changes to policy, practices, and products plus education can improve the PIVC first-stick success, dwell time, documentation, and staff safety.
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 | 0.149 | 0.126 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.024 | 0.012 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".