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Record W4392810788 · doi:10.53555/sfs.v10i6.2265

Assessment Of Nurses' Knowledge Towards Fluid And Electrolyte Administration At Surgical Wards In Hospitals

2023· article· en· W4392810788 on OpenAlexvenueno aff
Shaima Abdullah Aldossery, Badriah Ahmed Ali Anab, Sultan Aali Awwadh Alzaydi, Lolo Saleh Alhmed, Bdour Obeid alenazi, Afrah Talal Kulaib Alanazi, Salha Mohammed Abdulrahman Alshehri

Bibliographic record

VenueJournal of Survey in Fisheries Sciences · 2023
Typearticle
Languageen
FieldMedicine
TopicElectrolyte and hormonal disorders
Canadian institutionsnot available
Fundersnot available
KeywordsAdministration (probate law)MedicineElectrolyteIntravenous fluidMedical emergencyNursingAnesthesiaPolitical scienceChemistry

Abstract

fetched live from OpenAlex

This study aims to assess nurses' knowledge regarding fluid and electrolyte administration at surgical wards in hospitals. Adequate fluid and electrolyte management is crucial for the care of surgical patients to maintain fluid balance, prevent complications, and promote optimal recovery. Assessing nurses' knowledge in this area can identify areas for improvement and guide targeted educational interventions. The study utilized a cross-sectional design, collecting data through a questionnaire specifically developed to assess nurses' knowledge of fluid and electrolyte administration. The sample consisted of nurses working at surgical wards in hospitals. Data analysis involved descriptive statistics and inferential tests to examine potential relationships between demographic variables and nurses' knowledge.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.081
GPT teacher head0.359
Teacher spread0.277 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2023
Admission routes1
Has abstractyes

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