Effect of the Specific Training Course for Competency in Doing Arterial Blood Gas Sampling in the Intensive Care Unit: Developing a Standardized Learning Curve according to the Procedure's Time and Socioprofessional Predictors

Abstract

Background: Standardization of clinical practices is an essential part of continuing education of newly registered nurses in the intensive care unit (ICU). The development of educational standards based on evidence can help improve the quality of educational programs and ultimately clinical skills and practices.

Objectives: The objectives of the study were to develop a standardized learning curve of arterial blood gas (ABG) sampling competency, to design a checklist for the assessment of competency, to assess the relative importance of predictors and learning patterns of competency, and to determine how many times it is essential to reach a specific level of ABG sampling competency according to the learning curve.

Design: A quasi-experimental, nonrandomized, single-group trial with time series design. Participants. All newly registered nurses in the ICU of a teaching hospital of Tehran University of Medical Sciences were selected from July 2016 to April 2018. Altogether, 65 nurses participated in the study; however, at the end, only nine nurses had dropped out due to shift displacement.

Methods: At first, the primary checklist was prepared to assess the nurses' ABG sampling practices and it was finalized after three sessions of the expert panel. The checklist had three domains, including presampling, during sampling, and postsampling of ABG competency. Then, 56 nurses practiced ABG sampling step by step under the supervision of three observers who controlled the processes and they filled the checklists. The endpoint was considered reaching a 95 score on the learning curve. The Poisson regression model was used in order to verify the effective factors of ABG sampling competency. The importance of variables in the prediction of practice scores had been calculated in a linear regression of R software by using the relaimpo package.

Results: According to the results, in order to reach a skill level of 55, 65, 75, 85, and 95, nurses, respectively, would need average ABG practice times of 6, 6, 7, 7, and 7. In the linear regression model, demographic variables predict 47.65 percent of changes related to scores in practices but the extent of prediction of these variables totally decreased till 7 practice times, and in each practice, nurses who had the higher primary skill levels gained 1 to 2 skill scores more than those with low primary skills.

Conclusions: Utilization of the learning curve could be helpful in the standardization of clinical practices in nursing training and optimization of the frequency of skills training, thus improving the training quality in this field. This trial is registered with NCT02830971.

Conflict of interest statement

The authors declare that they have no conflicts of interest.

Copyright © 2021 Amir Vahedian-Azimi et al.

Figures

Figure 1

Study design for developing and testing a standardized learning curve for ABG sampling competency for nurses.

Figure 2

Box plot for scores for mastery in arterial blood gas.

Figure 3

Mean score for mastery in arterial blood gas (learning curve).

Figure 4

Mean change in score for mastery in arterial blood gas among times of experience.

Figure 5

Poisson model accuracy in predicting the number of times essential for mastery in arterial blood gas according to the cutoff point of the learning curve.

Figure 6

Distribution of time essential for mastery in arterial blood gas according to the cutoff point of the learning curve.

Figure 7

Percentile curve for scores for mastery in arterial blood gas. This graph presents the rate of increasing skill scores of nurses according to the number of practices. The black line indicated the median of scores, the red line indicated the percentile between 3 and 97%, and the green line indicated the percentile from 15% to 85%. The percentile from 3-15 is assumed the weak level, 15-50% is assumed the middle level, 50-85% is assumed a good level, and 85-90% is assumed a well level.