Evidence map›Paper›PMID 40264200›Full record

ArticleBMC nursing2025

Development and psychometric evaluation of the artificial intelligence attitude scale for nurses.

Tuğba Öztürk Yıldırım, Mesut Karaman

Registry-linked trialAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC nursing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07631585 (Longitudinal Pre-Post Patient AI Trust Dynamics in Orthopedic Outpatients), which is not on this map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
–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.

NCT07631585 recruitingnot on this mapstarted 2026, after this paper: background citation

Longitudinal Pre-Post Patient AI Trust Dynamics in Orthopedic Outpatients: A Mixed-Methods Observational Study With Matched Physician-Patient Dyads

TypeobservationalSponsorUtku GürhanRan2026 to 2027Enrolled180ConditionsPatient Health Information Seeking Behavior, Trust, Health Literacy, Orthopedics
3 · Its place in the literature

Who cites it

15 citing papers in PubMed.

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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

2 authors.

Tuğba Öztürk YıldırımNursing Department, Doğuş University, Istanbul, Türkiye. tugba.ozturkyildirim@gmail.com.ORCID https://orcid.org/0000-0002-6853-8996
Mesut KaramanDepartment of Business Administration, Institute of Social Sciences, Sivas Cumhuriyet University, Sivas, Türkiye.ORCID https://orcid.org/0000-0002-7584-0800

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSince artificial intelligence is transforming healthcare, targeted interventions aimed at optimizing its integration and use in clinical settings requires the assessment of nurses' attitudes towards AI.

aimTo develop and validate an Artificial Intelligence Attitude Scale specifically for Turkish nurses.

methodThis methodological study was conducted between October 2024 and December 2024, and its sample consisted of 678 nurses working in Turkey. The item pool was developed through a comprehensive literature review. Data analysis included descriptive statistics, item analysis, and exploratory and confirmatory factor analyses, as well as assessments of convergent and divergent validity, correlation analysis, internal consistency reliability, and test-retest reliability.

resultsThe content validity index for the items ranged from 0.85 to 1.00. Exploratory factor analysis revealed that the eigenvalues for four factors were greater than one, and these four factors accounted for 77.28% of the total variance. The scale demonstrated an acceptable model fit, with a goodness of fit index of 0.921 and a root mean square error of approximation (RMSEA) of 0.064. Cronbach's alpha coefficients ranged from 0.93 to 0.95 across the subscales, indicating high internal consistency, with the scale showing convergent and divergent validity. In addition, the Artificial Intelligence Attitude Scale for Nurses was found to have high test-retest reliability. This study may offer valuable insights into nurses' attitudes toward digital technologies, thereby informing the trajectory of digital transformation in healthcare services.

Indexed as

Artificial intelligenceAttitudeNursesPsychometric evaluationScale development

Identifiers

PMID40264200
PMCPMC12013020

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Registered trials

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.