The Clinical Nurse Specialist Role and its Relevance to Vascular Access: A Canadian Perspective
Bibliographic record
Abstract
Highlights The adoption of a VA-CNS has demonstrable benefits on organizational and patient outcomes. The VA-CNS is a cost-effective option for health systems, demonstrating a positive payback of $417,525 to the organization in a 1-year period. This methodological approach to the evaluation of the VA-CNS may be useful for other clinicians in determining the effects and/or cost effectiveness of new positions on patient and organizational outcomes. Abstract Purpose: The purpose of this quality improvement study was to examine the impact of a Vascular Access Clinical Nurse Specialist (VA-CNS) on patient and organizational outcomes. Description of the Project/Program: The VA-CNS role was created and implemented at an acute care hospital in Thunder Bay, Ontario, Canada. The VA-CNS collected data on clinical activities and interventions performed from April 1 to March 29, 2019. The dataset and its associated qualitative clinical outcomes were analyzed using deductive content analysis. Furthermore, a cost analysis was performed by the hospital accountant on these clinical outcomes. Outcome: Over a 1-year period, there were 547 patients protected from an unwarranted peripherally inserted central catheter (PICC) insertion among 302 patient consultations for the VA-CNS. A total of 322 ultrasound-guided peripheral intravenous catheters were inserted and 45 PICC insertions completed at the bedside. The cost associated with the 547 patients not receiving a PICC line result in an estimated savings of $113,301. The VA-CNS role demonstrated a positive payback of $417,525 to the organization. Conclusion: The results of this quality improvement project have demonstrated the positive impacts of the VACNS on patient and organizational outcomes. This role may be of benefit and worth its adoption for other health systems with similar patient populations.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".