Kıymaç Sarı, Merve

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Profile URL
https://hdl.handle.net/20.500.14627/633
Name Variants
Sari, Merve Kiymac
Job Title
Doktor Öğretim
Email Address
merve.sari@fbu.edu.tr
Main Affiliation
Hemşirelik Bölümü
Status
Website
ORCID ID
0000-0002-3845-3385
Scopus Author ID
57843966000
Turkish CoHE Profile ID
Google Scholar ID
3z4cYtsAAAAJ
WoS Researcher ID
KHZ-6652-2024
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photo_381708.jpg.JPG (4.61 MB)

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Scholarly Output

3

Articles

1

WoS Citation Count

0

Scopus Citation Count

0

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0

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2024 - 20263
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Now showing 1 - 3 of 3
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    Article
    The Relationship Between Nurses Anxiety and Attitudes Towards Artificial Intelligence and Examination of Influencing Factors
    (BMC, 2026) Nirgiz, Cansu; Sari, Merve Kiymac; Cayli, Nazan
    Aim to explore the relationships between nurses'anxiety and attitudes toward artificial intelligence (AI) and the factors associated with them. Background Although 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 T & uuml;rkiye, 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. Methods This 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 (rho = 0.25, alpha = 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 Fenerbah & ccedil;e University Ethics Committee, and informed consent was obtained digitally. Results Nurses 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. Conclusion The 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.
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    Conference Object
    Evaluation of Fatigue and Self-Care Management in Individuals With COPD
    (European Respiratory Soc Journals Ltd, 2025) Mirzaoglu, Helin; Sari, Merve Kiymac
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    Conference Object
    Nurse Led Telehealth Interventions in Copd Patients
    (European Respiratory Soc Journals Ltd, 2024) Sari, Merve Kiymac; Yilmaz, Melis Sen; Akyil, Rahsan Cevik
    [No Abstract Available]