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.


Journal

BioMed research international
ISSN: 2314-6141
Titre abrégé: Biomed Res Int
Pays: United States
ID NLM: 101600173

Informations de publication

Date de publication:
2021
Historique:
received: 13 08 2020
revised: 13 01 2021
accepted: 27 01 2021
entrez: 25 2 2021
pubmed: 26 2 2021
medline: 4 6 2021
Statut: epublish

Résumé

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. 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. A quasi-experimental, nonrandomized, single-group trial with time series design. 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. 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. 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.

Sections du résumé

BACKGROUND 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 OBJECTIVE
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 METHODS
A quasi-experimental, nonrandomized, single-group trial with time series design.
METHODS 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 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 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.

Identifiants

pubmed: 33628776
doi: 10.1155/2021/2989213
pmc: PMC7899780
doi:

Banques de données

ClinicalTrials.gov
['NCT02830971']

Types de publication

Clinical Trial Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

2989213

Informations de copyright

Copyright © 2021 Amir Vahedian-Azimi et al.

Déclaration de conflit d'intérêts

The authors declare that they have no conflicts of interest.

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Auteurs

Amir Vahedian-Azimi (A)

Trauma Research Center, Nursing Faculty, Baqiyatallah University of Medical Sciences, Tehran, Iran.

Farshid Rahimi-Bashar (F)

Department of Anesthesiology and Critical Care, School of Medicine, Hamadan University of Medical Sciences, Hamadan, Iran.

Mohamad-Amin Pourhoseingholi (MA)

Department of Health System Research, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

Mahmood Salesi (M)

Chemical Injuries Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran.

Morteza Shamsizadeh (M)

Chronic Diseases (Home Care) Research Center, Hamadan University of Medical Sciences, Hamadan, Iran.

Tannaz Jamialahmadi (T)

Department of Food Science and Technology, Quchan Branch, Islamic Azad University, Quchan, Iran.
Department of Nutrition, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.

Keivan Gohari-Moghadam (K)

Medical ICU and Pulmonary Unit, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran.

Amirhossein Sahebkar (A)

Biotechnology Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran.
Applied Biomedical Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
School of Pharmacy, Mashhad University of Medical Sciences, Mashhad, Iran.

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Classifications MeSH