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Record W4385290298 · doi:10.12968/bjon.2023.32.14.s36

Understanding nursing perceptions of intravenous fluid management practices

2023· article· en· W4385290298 on OpenAlexaffabout
Sarah Crowe

Bibliographic record

VenueBritish Journal of Nursing · 2023
Typearticle
Languageen
FieldMedicine
TopicHemodynamic Monitoring and Therapy
Canadian institutionsSurrey Memorial HospitalFraser Health
Fundersnot available
KeywordsMedicineNursingPromotion (chess)Nursing managementMEDLINEIntravenous fluidSurgery

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.963
Threshold uncertainty score0.354

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.130
GPT teacher head0.385
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2023
Admission routes2
Has abstractyes

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