Evidence map›Paper›PMID 40542340›Full record

ArticleBMC nursing2025

Embracing artificial intelligence in nursing: exploring the relationship between artificial intelligence-related attitudes, creative self-efficacy, and clinical reasoning competency among nurses.

Amal Diab Ghanem Atalla, Marwa Abd El-Gawad Mousa, Ebtsam Aly Abou Hashish, Naglaa Abdelaziz Mahmoud Elseesy, Aziza Ibrahim Abd El Kader Mohamed, Samia Mohamed Sobhi Mohamed

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Article in BMC nursing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed, 1 pooled it
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

30 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Amal Diab Ghanem AtallaDepartment of Nursing Administration, Faculty of Nursing, Alexandria University, Alexandria, Egypt. aml.diab@alexu.edu.eg.ORCID https://orcid.org/0000-0001-8903-1601
Marwa Abd El-Gawad MousaDepartment of Psychiatric and Mental Health Nursing, Faculty of Nursing, Alexandria University, Alexandria, Egypt.
Ebtsam Aly Abou HashishDepartment of Nursing Administration, Faculty of Nursing, Alexandria University, Alexandria, Egypt.
Naglaa Abdelaziz Mahmoud ElseesyDepartment of Nursing Administration, Faculty of Nursing, Alexandria University, Alexandria, Egypt.
Aziza Ibrahim Abd El Kader MohamedDepartment of Medical-Surgical Nursing, Faculty of Nursing, Badr University, Cairo, Egypt.
Samia Mohamed Sobhi MohamedDepartment of Nursing Administration, Faculty of Nursing, Alexandria University, Alexandria, Egypt.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAs 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.

aimThis study aimed to explore the relationship between nurses' AI-related attitudes, creative self-efficacy, and clinical reasoning competency.

methodsA 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.

resultsThe 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.

Indexed as

AI attitudesArtificial intelligenceClinical reasoning competencyCreative Self-efficacyNurses

Identifiers

PMID40542340
PMCPMC12181922

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.