ArticleJournal of advanced nursing2026
The Mediation of AI Trust on AI Uncertainties and AI Competence Among Nurses: A Cross-Sectional Study.
Article in Journal of advanced nursing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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.
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.
Who cites it
4 citing papers in PubMed.
- The Double-Edged Sword Effect of AI Application Among Clinical Nurses Based on the Job Demands-Resources Model: Qualitative Study.JMIR nursing · 2026Article
- The Design-Driven Innovation Path of Human-Centered Artificial Intelligence in the Field of Healthcare: Theory, Practice, and Future Prospects.Healthcare (Basel, Switzerland) · 2026Review
- The Mediation of AI Trust on AI Uncertainties and AI Competence Among Nurses: A Cross-Sectional Study.Journal of advanced nursing · 2026Article
- AI literacy as a potential mediator between attitude and self-efficacy among PICU nurses: a cross-sectional study.Frontiers in public health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimThis study aimed to validate the mediating role of nurses' AI trust in the relationship between AI uncertainties and AI competence.
designA cross-sectional study.
methodsA purposive sample of 550 registered nurses with at least 1 year of clinical experience from three tertiary and two secondary hospitals in Jinan and Hangzhou, China, was used. Data were collected using structured questionnaires assessing AI uncertainty, trust and competence. Demographic data included gender, age, education level, years of clinical experience, professional title and hospital level. Mediation analysis.
resultsMost nurses were from tertiary hospitals (88.9%), held a bachelor's degree (87.6%), and had over 6 years of experience. The mediating role of AI trust between AI uncertainties and AI competence is validated. AI uncertainties affected AI trust (B = 0.39, p < 0.0001), explaining 10% of the variance. AI uncertainties and AI trust affected AI competence (B = 0.25 and 0.67, p < 0.0001), explaining 63% of the variation. AI trust's total effect was 0.51, comprising direct and indirect effects of 0.25 and 0.26, respectively.
conclusionHospitals can reduce uncertainty through an AI-transparent decision-making process, providing clinical examples of AI and training nurses to use AI, thereby increasing trust. Second, AI systems should be designed to consider nurses' psychological safety needs. Hospital administrators utilise optimised AI technology training and promotional techniques to mitigate nurses' resistance to AI and enhance their positive perceptions of AI competence through trust-building mechanisms. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: Impact: Enhancing nurses' AI trust can reduce uncertainty and improve their competence in clinical use. Strategies such as transparency, explainability and training programmes are crucial for improving AI implementation in healthcare. NO PATIENT OR PUBLIC CONTRIBUTION: This study focused solely on clinical nurses and did not include patients or the public. REPORTING
methodThe study adhered to STROBE guidelines.
Indexed as
Identifiers
What OpenQuestion holds
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.