Understanding nursing perceptions of intravenous fluid management practices
Bibliographic record
Abstract
PURPOSE: Intravenous (IV) fluids are routinely used in hospitalized patients. As IV fluids are an everyday occurrence, their importance is often overlooked. Many patients receive large volumes of fluid during resuscitation to aid in the promotion of tissue perfusion. Nurses regularly administer IV fluids as part of maintenance infusions or as life-saving therapies and, therefore, need to understand these fluids' impact on their patients. Understanding nurses' existing perceptions of IV fluid management practices are crucial to improving practice. METHODS: This study used an online survey to gather information on nursing perceptions of IV fluids. Four hundred and sixty-two Canadian nurses from diverse backgrounds were surveyed, including registered nurses, licensed practical nurses and student nurses. RESULTS: The study found that the majority of participants agreed that IV fluids, including type, amount, and rationale for infusion, were important. They also agreed that fluids could impact patient outcomes. However, the study found that, despite recognizing the value and importance of fluid management, many nurses struggled with recognizing how to determine a patient's fluid status versus fluid responsiveness. CONCLUSION: This study supports improving nursing education to understand better the differences between fluid volume status and volume responsiveness. Our study also provides evidence that nurses need access to more sophisticated tools to conduct dynamic assessments and better meet patients' needs.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".