Effectiveness of implementing evidence based practices guidelines regarding blood transfusion on quality of nursing care and patients' safety in Pediatric Units
Bibliographic record
Abstract
Background and objective: The new trend that widely accepted in health care institutions is to implement an evidence-based practice. Health facilities frequently integrate standards of practice that reveal current best evidence to increase patients’ outcomes and consequently decrease hospital cost. Transfusion of blood is a cornerstone in managing many critically ill children. However, nurses have a chief role in transfusing blood and their knowledge and performance are important for them to transfuse blood safely and efficiently. Aim: Evaluate the effectiveness of implementing evidence based nursing practices guidelines on quality of nursing care and patients' safety as regards blood transfusion to improve transfusion practices and ensure safety.Methods: A quasi-experimental design. Settings: This study was conducted at Pediatric Intensive Care Unit, Neonatal Intensive Care Unit, Emergency Room, Medical and Surgical Wards, Hematology/Oncology Units in Children Hospital affiliated to Ain Shams University Hospitals. Sample: A convenience sample composed of 95 pediatric nurses, whom were willingness to participate in the study and 78 children whom were receiving blood transfusion. Tools: I. A Self-Administered Questionnaire Sheet to assess nurses’ knowledge regarding blood transfusion; II. Child’s Medical Record to collect data about child’s health status; III. An Observational Competence Checklist to assess the quality of actual nurses’ practices about Blood Transfusion; and IV. Evidence Based Nursing Practices Guidelines of Blood Transfusion that was described the EBNP guidelines that provide a standardized approach for transfusion (before and after).Results: The studied nurses’ knowledge and practices regarding to blood transfusion were improved and reflected a highly significant differences before and after guidelines implementation.Conclusions: The present study concluded that studied nurses showed an improvement in their knowledge and practices regarding blood transfusion after implementation of evidence based nursing practices guidelines. Recommendation: It is essential that all nurses who administer blood transfusion for children should complete periodic in-services training programs to keep them up to date regarding to safe and efficient administration of blood transfusion.
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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 | 0.028 | 0.105 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".