The effect of scenario based teaching for critical care nurses and physicians on their knowledge of fluid creep
Bibliographic record
Abstract
Introduction: Fluid resuscitation is a major component of the acute management of critically ill patients. The phenomenon of providing excessive fluid resuscitation volumes has been termed “fluid creep”. Today, the science of nursing becomes more complex. Accurate fluid balance assessment and recording is important component of nursing care that assures patient’s safety especially in critically ill patients. The aim of the work is to examine the effect of scenario based teaching for critical care nurses and physicians on their knowledge of fluid balance & fluid creep.Methods: The study design: pre & posttest research design. Setting: This study was implemented in general, trauma, obstetric and burn intensive care units (ICUs) at Assiut university Hospital-Assiut-Egypt. Subjects: 35 critical care nurses and 29 intensive care physicians were drawn from the previously mentioned ICUs. Methodology: A pre & posttest questionnaire of nurses’ and physicians’ knowledge, perception and satisfaction regarding fluid creep and fluid balance was adapted from the articles and was used in data collection before and after the application of scenario based teaching. This questionnaire was implemented on two phases (pre and after the teaching program). The data was collected from January 2018 to July 2018.Results: There is a considerable improvement in participants’ knowledge and perception concerning fluid balance and fluid creep after applying the scenario based teaching (p value < .001).Conclusions: Nowadays, nurses and physicians need advanced level of knowledge to be able to deal with the physiological changes that occur in critically ill.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".