Javascript must be enabled to continue!
Embracing artificial intelligence in nursing: exploring the relationship between artificial intelligence-related attitudes, creative self-efficacy, and clinical reasoning competency among nurses
View through CrossRef
Abstract
Background
As artificial intelligence (AI) becomes an integral part of healthcare, nursing practice is rapidly evolving, requiring a deeper understanding of how nurses’ attitudes toward AI influence essential competencies such as creative self-efficacy and clinical reasoning competency, both of which are crucial for delivering safe and effective patient care.
Aim
This study aimed to explore the relationship between nurses’ AI-related attitudes, creative self-efficacy, and clinical reasoning competency.
Methods
A cross-sectional descriptive-correlational design was employed, involving a convenience sample of 380 nurses working in critical care units at a university-affiliated hospital in Egypt. Data were collected using three validated instruments: the Nurses’ Artificial Intelligence Attitudes Scale, the Creative Self-Efficacy Scale, and the Clinical Reasoning Competency Scale. Data analysis included both descriptive and inferential statistics.
Results
The majority of nurses demonstrated high levels of AI-related attitudes and clinical reasoning competency, while moderate levels of creative self-efficacy were observed. A strong positive correlation was found between AI attitudes and both creative self-efficacy and clinical reasoning competency (r = 0.559 and r = 0.728, p < 0.001, respectively). Regression analysis confirmed that AI attitudes were significant predictors of both creative self-efficacy and clinical reasoning competency, explaining 37.4% and 56.5% of their variance, respectively. Additionally, educational qualifications and years of nursing experience were identified as significant factors influencing these competencies.
Conclusion and Implications
Positive attitudes toward artificial intelligence (AI) play a crucial role in enhancing nurses’ creative self-efficacy and clinical reasoning competency. Therefore, fostering positive perceptions of AI and providing targeted training are vital to prepare nurses for AI-integrated clinical environments. Integrating AI-focused content into nursing education and promoting continuous professional development are key strategies to strengthen nurses’ readiness to engage with AI-driven healthcare. Additionally, healthcare organizations and nursing leaders should create supportive environments that encourage AI adoption while preserving the principles of patient-centered care.
Clinical trial number
Not applicable.
Springer Science and Business Media LLC
Title: Embracing artificial intelligence in nursing: exploring the relationship between artificial intelligence-related attitudes, creative self-efficacy, and clinical reasoning competency among nurses
Description:
Abstract
Background
As artificial intelligence (AI) becomes an integral part of healthcare, nursing practice is rapidly evolving, requiring a deeper understanding of how nurses’ attitudes toward AI influence essential competencies such as creative self-efficacy and clinical reasoning competency, both of which are crucial for delivering safe and effective patient care.
Aim
This study aimed to explore the relationship between nurses’ AI-related attitudes, creative self-efficacy, and clinical reasoning competency.
Methods
A cross-sectional descriptive-correlational design was employed, involving a convenience sample of 380 nurses working in critical care units at a university-affiliated hospital in Egypt.
Data were collected using three validated instruments: the Nurses’ Artificial Intelligence Attitudes Scale, the Creative Self-Efficacy Scale, and the Clinical Reasoning Competency Scale.
Data analysis included both descriptive and inferential statistics.
Results
The majority of nurses demonstrated high levels of AI-related attitudes and clinical reasoning competency, while moderate levels of creative self-efficacy were observed.
A strong positive correlation was found between AI attitudes and both creative self-efficacy and clinical reasoning competency (r = 0.
559 and r = 0.
728, p < 0.
001, respectively).
Regression analysis confirmed that AI attitudes were significant predictors of both creative self-efficacy and clinical reasoning competency, explaining 37.
4% and 56.
5% of their variance, respectively.
Additionally, educational qualifications and years of nursing experience were identified as significant factors influencing these competencies.
Conclusion and Implications
Positive attitudes toward artificial intelligence (AI) play a crucial role in enhancing nurses’ creative self-efficacy and clinical reasoning competency.
Therefore, fostering positive perceptions of AI and providing targeted training are vital to prepare nurses for AI-integrated clinical environments.
Integrating AI-focused content into nursing education and promoting continuous professional development are key strategies to strengthen nurses’ readiness to engage with AI-driven healthcare.
Additionally, healthcare organizations and nursing leaders should create supportive environments that encourage AI adoption while preserving the principles of patient-centered care.
Clinical trial number
Not applicable.
Related Results
Characteristics and processes of registered nurses’ clinical reasoning and factors relating to the use of clinical reasoning in practice: a scoping review
Characteristics and processes of registered nurses’ clinical reasoning and factors relating to the use of clinical reasoning in practice: a scoping review
Objective:
The objective of this review was to examine the characteristics and processes of clinical reasoning used by registered nurses in clinical practice, and to id...
<b>ADOPTING ARTIFICIAL INTELLIGENCE (AI) IN NURSING CARE: THE INTERPLAY OF ATTITUDES, SELF-BELIEF, AND CLINICAL REASONING AT NISHTAR HOSPITAL, MULTAN</b>
<b>ADOPTING ARTIFICIAL INTELLIGENCE (AI) IN NURSING CARE: THE INTERPLAY OF ATTITUDES, SELF-BELIEF, AND CLINICAL REASONING AT NISHTAR HOSPITAL, MULTAN</b>
Background: Artificial intelligence (AI) is increasingly influencing clinical practice by enhancing diagnostic precision, improving patient safety, and facilitating informed decisi...
Nurses are leaving the nursing profession: A finding from the willingness of the nurses to stay in the nursing profession among nurses working in selected public hospitals of Wollega Zones, Oromia, Ethiopia
Nurses are leaving the nursing profession: A finding from the willingness of the nurses to stay in the nursing profession among nurses working in selected public hospitals of Wollega Zones, Oromia, Ethiopia
Background: The willingness of nurses to stay in nursing profession is nurses stay in the nursing profession without having intention to shift their works to other professions. In ...
OA27 Growth of the UK and Ireland paediatric rheumatology nurses’ group
OA27 Growth of the UK and Ireland paediatric rheumatology nurses’ group
Abstract
Introduction/Background
The Paediatric Rheumatology Clinical Nurse Specialist often has to manage a large caseload of c...
Living nursing values: A collective case study
Living nursing values: A collective case study
<p>Distinctive humanistic values are foundational in professional nursing practice, commonly shared by members of the profession and the mainstay of how nurses act. The found...
Nurses’ informatics competency assessment of health information system usage: a cross-sectional survey
Nurses’ informatics competency assessment of health information system usage: a cross-sectional survey
Abstract
Background: Nurses’ informatics competencies affect their use of health information systems (HIS). Informatics competencies are professional requirements for regis...
A cross‐sectional study of the relationship between missed nursing care and conscientious intelligence in hospital nurses
A cross‐sectional study of the relationship between missed nursing care and conscientious intelligence in hospital nurses
AbstractAimTo examine the relationship between missed nursing care and conscientious intelligence.BackgroundMissed nursing care is a globally common patient safety issue that threa...
Nurses’ competency in electrocardiogram interpretation in acute care settings: A systematic review
Nurses’ competency in electrocardiogram interpretation in acute care settings: A systematic review
Abstract
Aims
Identify and synthesize evidence of nurses’ competency in electrocardiogram interpretation in acute care se...

