Evidence map›Paper›PMID 41501735›Full record

ArticleBMC nursing2026

The relationship between nurses' anxiety and attitudes towards artificial intelligence and examination of influencing factors.

Cansu Nirgiz, Merve Kıymaç Sarı, Nazan Çaylı

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In one paragraph

Article in BMC nursing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

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

  1. 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

3 authors.

Cansu NirgizDepartment of Nursing, Faculty of Health Sciences, Fenerbahçe University, İstanbul, Türkiye. cansunirgiz@hotmail.com.ORCID http://orcid.org/0000-0001-9595-2853
Merve Kıymaç SarıDepartment of Nursing, Faculty of Health Sciences, Fenerbahçe University, İstanbul, Türkiye.ORCID http://orcid.org/0000-0002-3845-3385
Nazan ÇaylıSamsun Medicana Health Group, Samsun, Türkiye.ORCID http://orcid.org/0000-0002-9116-6713

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimTo explore the relationships between nurses’ anxiety and attitudes toward artificial intelligence (AI) and the factors associated with them.

backgroundAlthough AI technologies are increasingly integrated into healthcare, research exploring nurses’ psychological readiness and emotional responses to AI remains limited. Existing studies have primarily focused on nursing students or general healthcare professionals, leaving a gap in understanding how practicing nurses perceive and emotionally adapt to AI within real clinical environments—particularly in Türkiye, where digital transformation in healthcare is accelerating. Addressing this gap is essential, as nurses play a pivotal role in ensuring the safe and ethical implementation of AI-driven tools in patient care.

methodsThis descriptive and correlational study included 562 nurses from 14 branches of a private hospital chain across seven Turkish cities between November 2024 and January 2025. This sample was selected because it represents nurses actively engaged in clinical decision-making within healthcare systems that are rapidly adopting AI technologies. According to a power analysis performed in G*Power (ρ = 0.25, α = 0.05, power = 0.95), the required sample size was 202 participants. Data were collected through an online questionnaire comprising a Descriptive Information Form, the AI Anxiety Scale, and the General Attitudes toward AI Scale. Descriptive statistics, independent samples t-tests, ANOVA (F), Tukey post hoc test, Pearson correlation, and multiple regression analyses were conducted. Ethical approval was obtained from the Fenerbahçe University Ethics Committee, and informed consent was obtained digitally.

resultsNurses reported moderate anxiety levels and generally positive attitudes toward AI. Male nurses showed an association with lower anxiety levels and higher positive attitude scores than female nurses. Single individuals and those with higher levels of education showed higher positive attitudes toward AI. Those with 0–3 years of experience in the profession were associated with lower anxiety and higher positive attitude scores. Nurses who used AI in practice, were knowledgeable about its use, or perceived it as reliable showed a relationship with lower anxiety and more positive attitudes. Regression analysis showed that each one-unit increase in the learning and AI configuration subscales of the AI Anxiety Scale was associated with a 0.740- and 0.716-point lower score in the total attitude score, respectively.

conclusionThe findings suggest that lower levels of anxiety related to learning and AI configuration are associated with more positive attitudes toward AI. Addressing these specific anxiety domains may be related to the successful integration of AI technologies into clinical practice and could be linked to the digital transformation in healthcare. RELEVANCE TO CLINICAL PRACTICE: Nurses’ perceptions of AI directly influence their willingness to use AI-enabled systems in patient care. Supporting nurses’ digital literacy and confidence in AI can reduce anxiety, enhance patient safety, and promote effective clinical decision-making. REPORTING

methodReported in accordance with the STROBE guidelines. PATIENT OR PUBLIC CONTRIBUTION: By reducing nurses’ anxiety and fostering positive attitude toward AI, this study underscores potential benefits for patient care, including improved decision-making, patient safety, and healthcare efficiency.

Indexed as

AnxietyArtificial intelligenceAttitudeNurses

Identifiers

PMID41501735
PMCPMC12871030

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LicenceCC BY-NC-ND
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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.