Effect of Self –Learning Package on Nurses' Knowledge and Practice Regarding Arterial Blood Gases Analysis for Critically Ill Patients

Abstract

Background: Arterial blood gases analysis is major part of the diagnosis and management of the oxygenation status, acid base balance, and ventilation in critically ill patients. Aim: The study aimed to evaluate the effect of self-learning package on nurses' knowledge and practices regarding arterial blood gases analysis for critically ill patients. Subject and methods: Quasi experimental research design conducted at three intensive care units affiliated to Zagazig University Hospital; Surgical, Anesthetic, Cardio and Chest intensive care units, using pretest/posttest and follow-up test approach. Subject: A convenient sample of all available nurses with total number 60 was recruited from the selected units. Tools of data collection: Two tools were used for collecting data: Interviewing questionnaire to assess nurses' knowledge and observational checklist to assess nurses' practice regarding arterial blood gases analysis for critically ill patients. Results: There was a statistically significant positive correlation between total knowledge and total practice scores of the studied nurses. Preprogram (93.3%) of the studied nurses had unsatisfactory level of knowledge while in post program (91.7%), and in follow-up (86.7%) had satisfactory level, Regarding practice , pre-program (86.7%)of the studied nurses had unsatisfactory level , post-program (93.3) had satisfactory, and while follow up (86.7%) had satisfactory level. Conclusion: The study finding concluded that, implementation of self-learning package had highly statistical significance positive effect on improving nurses' knowledge and practice scores regarding arterial blood gases analysis. The main study recommendation: The importance of conduction the self-learning package in a wider field including all hospitals to raise the efficiency of nursing care provided, and a further study to be carried out in different settings on a larger sample for a wider utilization of the developed package, in order to achieve generalization of the results.